VT International Press Journal of Multidisciplinary Research and Review
Bridging Knowledge Across the Frontiers of Engineering and Technology “VTIPJMRR is a rigorously peer-reviewed, open-access international journal dedicated to advancing the frontiers of engineering, applied sciences, and emerging technologies through multidisciplinary scholarship and critical review.” The rapid convergence of engineering disciplines — from electronics and computing to civil infrastructure, energy systems, and biomedical technology — demands a publication platform that transcends traditional domain silos. The VT International Press Journal of Multidisciplinary Research and Review (VTIPJMRR) was founded to meet precisely this need: a high-quality, internationally visible forum where researchers, practitioners, and innovators can present original findings, comprehensive reviews, and cross-disciplinary perspectives that drive real-world progress. Published by VT International Press, this journal upholds the highest standards of academic integrity, double-blind peer review, and scientific rigor. Contributions are welcomed from academicians, industry professionals, and graduate researchers across all corners of the globe, reflecting our commitment to diversity, inclusion, and the universality of scientific inquiry.
VT International Press Journal of Multidisciplinary Research and Review (VTIPJMRR)
All articles published in VTIP-JMRR are made freely available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, others may freely share, distribute, and adapt the published work, provided appropriate credit is given to the original authors. By signing the copyright form, the author(s) warrant and represent that: ✓ The manuscript is original and has not been published previously in any journal, conference, or book (in whole or in part). ✓ The manuscript is not currently under consideration for publication elsewhere. ✓ All authors have read and approved the final manuscript and have agreed to this copyright transfer. ✓ The manuscript does not infringe upon any existing copyright, trademark, patent, or other intellectual property right of any third party. ✓ All data, figures, and tables presented are accurate, authentic, and have not been fabricated or falsified. ✓ Proper permissions have been obtained for any third-party material (images, figures, tables, text) included in the manuscript. ✓ The work complies with all applicable ethical guidelines, including those related to human and animal research. ✓ All sources of funding and conflicts of interest have been disclosed in the manuscript.
Formulation and Evaluation of Herbal Oral Care Products Using Alum and Clove Oil (Mouthwash)
Oral ulcers create severe pain in the oral mucosa and will severely limit eating and speaking. While conventional treatment of oral ulcers through topical anesthesia or steroid application can provide temporary symptom relief, these applications have the potential for long term adverse effects. This article describes the development of a herbal mouth wash comprised of Syzygium aromaticum (clove oil), KAl(SO₄)₂·12H₂O (alum), glycerin, sorbitol, citric acid, sodium benzoate, and sodium lauryl sulfate (SLS) designed to provide rapid relief from pain; reduce inflammation; enhance wound closure and facilitate oral hygiene. Syzygium aromaticum has been shown to be effective in providing significant antimicrobial/antifungal activity and analgesia due to its high eugenol content. Alum was chosen because it provides both astringency and hemostasis which enhance wound closure. Sorbitol and glycerine function as humectants to maintain moisture within the solution. Citric acid ensures the solution remains at an optimal pH for its intended use. Sodium benzoate functions as a preservative extending the shelf life of the product. SLS creates the foam component of this product and facilitates cleaning the teeth. Three different formulations (F1, F2 & F3) were developed, tested for their organoleptic characteristics, pH level, viscosity, foam volume created by each formula, and tested for antimicrobial properties utilizing the agar-well-diffusion technique. Additionally, all three formulas underwent accelerated-stability studies. The most stable formula (F3) had a pH level of 5.8, sufficient viscosity for oral use, created a zone of inhibition of 15mm around it when tested against several common pathogens found on the oral cavity and showed acceptable physical-chemical stability throughout the test period.
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Development and Physicochemical Evaluation of Herb-Infused Functional Cookies
The purpose of this investigation was to create and assess functional cookies made with Ashwagandha (Withania somnifera), Cinnamon (Cinnamomum verum), Ginger (Zingiber officinale), Mint (Mentha piperita) & Cardamom (Elettaria cardamomum). The growing number of individuals affected by lifestyle-related illnesses has led consumers to seek out functional foods which provide some type of health benefit in addition to fulfilling their nutritional needs. Cookies prepared from herbs are an exciting example of how modern food technology can combine the knowledge from traditional Ayurvedic pharmacology. The suggested formula will help prepare a cookie base that satisfies sensory requirements while providing the therapeutic effect associated with reducing stress through the use of the Ashwagandha as an adaptogen; enhancing digestive function through enhanced GI motility; stimulating or modulating immune response; and protecting against oxidative damage. Due to its proven prebiotic activity and antimicrobial properties honey was chosen for use as the natural sweetener and because it provided the fat component in the homemade butter. Consumer panel acceptance assessments of the organoleptic characteristics of each cookie were performed using a 9 point hedonic scale for measuring taste, texture, smell, appearance, and overall acceptability. Results indicated that consumer panels found cookies made with the specified proportions of herbs acceptable. Assessments were also completed on physio-chemical properties as well as shelf life. Overall, results from the assessment support the idea that cookies prepared from herbs are a viable alternative as a functional food that provides both nutritionally as well as therapeutically.
Autonomous Mobile Robots in Warehouse Automation: A Comprehensive Review of AI, Navigation, and Sensing Technologies Under Industry 4.0
Warehouse automation is becoming increasingly important as the use of e-commerce, global supply chains and Industry 4.0 continues to increase. In order to address this increasing importance, there is a pressing need for scalable, smart and adaptable warehouse automation systems. As such, the purpose of this review is to provide a comprehensive overview of the current state-of-the-art in using Autonomous Mobile Robots (AMRs) for materials handling. The AMR field has grown rapidly since 2020 and the number of peer reviewed articles related to it has increased significantly. Consequently, in addition to the growing interest in this field, we have synthesized 45 or so papers from six interdependent areas of research including; SLAM/autonomous navigation, deep learning/computer vision for object detection, path planning techniques/algorithms, obstacles avoidance techniques/methodologies, sensor fusion methods and artificial intelligence based inventory management. Through our systematic analysis of these papers we identified both significant advancements in each of the various sub-systems included within the AMR system, i.e., YOLO-based object detection was found to be accurate at approximately 96%, A* path planning accuracy was enhanced to 97% and LiDAR-Camera sensor fusion provided a 100% reliable results when testing for objects greater than 2 cm. However, despite the numerous advancements made in the individual sub-systems, existing solutions remain largely fragmented with limited evidence of unified frameworks being developed that support holistically managing warehouse operations. Furthermore, although some authors report success in deploying their solutions in large scale industrial settings, many others identify key challenges they faced including real-time computing constraints, dynamic obstacle handling issues, scalability limitations and inadequate validation of their solutions using data obtained in real world deployments. Therefore, through our analysis of these papers we were able to identify key research gap areas and propose potential future research direction including developing integrated and edge-computing enabled autonomous robot solutions that align with industry 4.0 standards. Our review also provides a consolidated platform for both researchers and practitioners wishing to develop next generation smart warehouse automation solutions.
AI-Integrated Mechanical Systems for Real-Time Industrial Emission Monitoring and Control: A Comprehensive Review
In this study we are reviewing how fast the development of artificial intelligence integrated mechanical systems in real time emissions can be used to monitor and manage emissions in industry. Traditional ways of collecting emissions have been by using manual samples, or sampling at intervals with use of Continuous Emissions Monitoring Systems (CEMS). Manual sampling and CEMS both provide delay in obtaining data and require high operational cost. Use of Artificial Intelligence (AI), Machine Learning (ML) and IoT technology allows for continuous collection of data, real-time analysis of collected data and proactive measures to reduce emissions. In addition to reviewing the progression of emissions monitoring from traditional methods through CEMS to current hybrid architectures combining AI and IoT technologies, this study evaluates machine learning techniques as follows: Regression Models; Decision Trees; Ensemble Methods; LSTM Networks; Deep Neural Networks. Additionally, this study identifies ongoing research issues: Limited Mechanical-Integration of AI Technology; Lack of Explainability of Predictions; No Closed Loop Automated Control. Lastly, the study provides a comparison of the performance of 12 different Machine Learning Techniques along with an evaluation of sensor technology, data pre-processing techniques, and Real-Time Dashboard Frameworks. Finally, this study suggests potential futures uses for Industry Applications such as Federated Learning; Edge AI; Digital Twin Integration.
Study on Intelligent Predictive Maintenance for Industrial Machinery: A Review
The application of Intelligent Predictive Maintenance (IPdM) is transforming industrial asset management through the intersection of Industry 4.0 technologies such as; Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), the Internet of Things (IoT) and Edge Computing. A systematic review of existing literature on current intelligent predictive maintenance paradigms, architectures and methods for use with industrial equipment is examined within this study. Additionally, the major technologies to be used for sensor based condition monitoring, fault detection/diagnosis, remaining useful life prediction and anomaly detection will be reviewed. This includes analysis of CNN's, LSTM's, random forests, GAN's, and explainable AI (xai). Data imbalance challenges in deploying models at the edge of an IoT network and how to achieve real time performance are presented. Emerging solutions to these issues are also addressed. Finally, this review presents several areas for potential research, which may include the development of federated learning systems, incorporation of digital twins into IPdM systems, and the design of hybrid edge cloud architectures.
Advanced Formulation Development and Multi-Criteria Evaluation of Multivitamin Nutraceutical Lollipops for Enhanced Pediatric Adherence
The ongoing challenge with ensuring children adhere to prescribed medications is primarily due to developmental dysphagia and increased sensitivities to bitter-tasting pharmaceuticals. The aim of this project was to develop multivitamin nutraceutical lollipop formulations as a new method of delivery for pediatric patients. These formulations contained the following amounts per serving: 40mg Vitamin C, 1mg each of Vitamins B1 & B2, 1mg of Vitamin B6, and 2mcg of Vitamin B12, 5mg of Zinc Gluconate. A "sugar glass" matrix method of developing these dosages, using Quality by Design (Qbd) principles, provided a systematic means of examining the combined effects of sucrose and liquid glucose on the mechanical strength and disintegration properties of the final dosage forms. Through thermal degradation studies it was determined that the temperature at which all heat sensitive vitamins would begin to degrade is 85°C (with a range of 80 - 90°C). At or above this temperature, the amount of active ingredient remaining in the product will be >98.5%. The optimized sugar to glucose ratio of 2.3:1 in formulation F3 resulted in a solid with higher hardness values (5.8±0.15kg/cm²) than previously studied ratios, lower friability (0.51%), and a nearly linear zero order release pattern (R²=0.98) over a 25-minute period when exposed to simulated saliva (pH 6.8). The longer duration of disintegration is beneficial for both covering up the metallic flavor associated with the ingestion of zinc gluconate and possibly allowing for some degree of absorption through the buccal mucosa thereby reducing the first pass effect through the liver. Packaging requirements were established based upon accelerated stability tests conducted according to ICH Q1A(R2) guidelines (40°C/75%RH for 3 months), requiring high barrier moisture resistant packaging. Overall, the data demonstrates that nutraceutical lollipops are a novel, patient-centered format for providing important pediatric micronutrient supplementation, transforming what has historically been viewed as an unpleasant medical intervention into an enjoyable consumer experience.
Social Cost-Benefit Analysis of Saudi Arabian Leadership's Decision for Non-Retaliation in the Iran War: A Civilization Continues in Peace and Harmony
The 2026 Iranian missile attacks against Saudi Arabian soil represented a watershed moment in the history of the Middle East geopolitics. Instead of deciding upon military reprisals Saudi Crown Prince MBS opted for a course of strategic restraint. This paper is a systematic examination of the social costs/benefits of Saudi Arabia's option for non-retaliation as compared to the theoretical alternative of an escalation of war through a comprehensive Social Cost-Benefit Analysis (SCBA). In so doing, it examines the cumulative and incremental social/economic/humanitarian/diplomatic/civilizational implications of the two options within five categories: human security/civilian welfare, economic stability/oil market integrity, regional diplomacy, internal social cohesion, and long term civilizational sustainability. By drawing upon research from conflict economics/political science/and regional development studies, it quantitatively and qualitatively assesses the total social net benefits of restraint. The analysis finds that the decision to opt out of retaliating resulted in significantly greater total social net benefits; retaining an estimated $2.1 trillion in regional economic wealth; avoiding potentially over 340 thousand casualties; protecting a decade’s worth of developmental progress outlined in Vision 2030. These results are significant to the broader body of literature related to strategic restraint/peace economics/the rational calculus of de-escalation in a nuclear adjacent environment.
Smart Dustbin: An Automated Waste Management Solution Using Arduino and Ultrasonic Sensing
The Smart Dustbin is a smart sensor-embedded waste management product which is going to help resolve problems with increasing solid waste management and disposal in urban areas. In this paper, we will describe the designing, developing and testing of the first version of Smart Dustbin that incorporates a HC-SR04 ultrasonic distance sensor, a SG90 servo motor and an Arduino Uno board. The project enables users to use the Smart Dustbin without touching its lid using an ultrasonic distance sensor to detect when waste has been placed at or below 20cm from the base of the bin. Once the sensor detects waste near the bin, it activates the servo motor to open the lid, and keeps it open for some time after opening then closes. After completing all stages of Human-Centred Design Thinking process including Empathise, Define, Ideate, Prototype and Test the prototype of Smart Dustbin was tested in a School Corridor and Residential Apartment. The results of testing demonstrated very positive responses from users towards aspects of hygiene, usability, and efficiency. In addition to the description of hardware design, circuit design and Arduino Code of the Smart Dustbin prototype, we have provided suggestions on potential developments to be made on the project including Solar Power Integration into the Smart Dustbin, Artificial Intelligence based Waste Sorting and connection to Municipal Dashboard.
From Intuition to Intelligence: A Data Science Framework for Modern Sports Analytics
The use of Data Science and Competitive Sports represents a major paradigm shift from using intuition to coach to making decisions based on evidence. In this study, a comprehensive Data Science Framework for Modern Sports Analytics is developed. It combines the collection of multi-modal data, the construction of signal-processing pipelines, the generation of advanced features through feature engineering, and the application of Machine Learning (ML) inference engines. A systematic review of the Literature from 2019 to 2025 was conducted to examine how Artificial Intelligence (AI) sub-disciplines such as supervised learning, unsupervised clustering, deep sequential models, and Explainable AI (XAI) were used in the three primary areas of Performance Optimization, Tactical Strategy, and Business Intelligence. Additionally, the challenges of integrating real-time data into the Data Science pipeline, the Ethical Implications of Collecting Biometric Data of Athletes, and the Increasing Use of Explainable AI to Build Trust with Coaches and Stakeholders are discussed. The proposed Data Science Framework addresses the gap that exists between Fragmented Single-Modality Research and Holistic Real-Time Models of Performance. Indicators of experimental validation across recent studies have shown Statistically Significant Improvements in Match Outcome Prediction Accuracy and Injury Risk Forecasting when Multi-Modal Pipelines were employed. As both a Structured Review and a Forward-Looking Blueprint for Researchers and Practitioners looking to Leverage Data Science for Competitive Advantage in Sports, this Study will be a valuable resource.
Suvarna Shalaka Agnikarma in the Management of Avabahuka (Frozen Shoulder): A Case Report
Agnikarma (thermal cautery), a traditional Ayurvedic parasurgical technique, is a successful treatment for conditions caused by aggravated Vata and Kapha doshas. Avabahuka, a Vata-vikara, is clinically associated with adhesive capsulitis, which is referred to as "frozen shoulder" in contemporary medicine. Shoola (pain), Stambha (stiffness), and Bahupraspanditahara (limited range of motion) at the Amsa Sandhi (shoulder joint) are its main clinical characteristics. Despite the existence of various conventional treatments, Agnikarma offers a unique and targeted therapeutic approach. A 48-year-old male presented with a 5-month history of severe pain, stiffness, and restricted movement in his right shoulder, clinically diagnosed as Avabahuka (adhesive capsulitis). A five-session Agnikarma therapy was administered to the right shoulder to manage Avabahuka via Vata-Kapha pacification. Post-treatment evaluation demonstrated marked reductions in localized pain and stiffness with a significant increase in Range of Motion (ROM). Agnikarma exhibits promising clinical efficacy in the management of Avabahuka.
Banking Security System Using Face and Liveness Detection Using Machine Learning and Image Processing
To provide safer user login experiences on ever-growing online banking services, it will be important to develop new, better forms of secure authentication. Traditional convolutional methods such as password, pin or one-time passwords are vulnerable to common attacks such as phishing and have been known to lose their validity when credentials are leaked. Biometric authentication based on face recognition offers both convenience and non-contact verification; however, traditional systems can be easily spoofed by fake photos, videos, or even 3-D masks. This paper presents an ML-based method to develop a more robust ID verification system via the use of image processing techniques along with deep learning techniques. An initial CNN will be used to extract facial features and recognize faces. In addition to the CNN, we also propose a liveness detection module that would detect the presence of natural behaviors associated with humans i.e., eye blinks, lip movements and small micro-expression changes in facial expressions to determine if the user is a living being or attempting to spoof the system. We plan to train and validate our model on publically available datasets like ORL, OULU, and CASIA to ensure that it will work under different lighting conditions, pose variations and textures. The system will utilize Python/TensorFlow/OpenCV/Keras and a MySQL DB. We will test this system’s accuracy/latency/resistance to spoofing attacks. Our experimental results show that the proposed system performs much better than other reported systems (over 98% recognition accuracy, very low FAR & very fast) for online/mobile banking applications. Therefore, this study demonstrates that the proposed system is an inexpensive way to improve security in banking infrastructures. Furthermore, due to the modularity of the proposed system, it can be easily integrated into all current banking platforms while complying with regulations related to data protection.
Comprehensive Framework for Data Science-Driven Decision-Making Across Healthcare, Finance, Marketing, and Supply Chain Domains
Data Science is transforming many industries by allowing organizations to use complex and large amounts of data to inform data-driven decision making. In this research paper we explore the methods and tools of Data Science in today's organizational environment, as well as its practical applications. Organizations that implement Data Science through predictive analytics, machine learning algorithms, and large-scale data platforms are able to increase their operational efficiency, make better strategic plans, and improve decision quality. In addition to examining how Data Science is applied in healthcare, marketing, finance, and supply chains, we evaluated the performance of predictive models. Several challenges were identified, including data quality, model interpretability, privacy, algorithmic fairness, and systems integration. Ethical issues and governance frameworks promoting responsible development of AI and analytics are also discussed. Findings confirmed that organizations adopting data-driven approaches achieve significant competitive advantages, improved resource allocation, and support long-term evidence-based decision-making.
Optimization of the Hard Turning Process by Artificial Neural Networks: A Review
Hard turning is a precision machining process employed as a finish operation on hardened alloy steels (42–68 HRC), widely used as an alternative to cylindrical grinding. The complexity and non-linearity of the hard turning process—governing surface roughness, cutting forces, tool wear, and cutting temperature—make traditional analytical modeling inadequate. Artificial neural networks (ANNs) have emerged as powerful data-driven tools capable of modeling and optimizing these intricate relationships with high predictive accuracy. This review systematically surveys peer-reviewed research published between 2005 and 2025, with emphasis on studies from 2018–2025, examining the application of ANN-based models to optimize hard turning parameters. The review covers ANN architectures employed (feedforward, radial basis function, deep neural networks, and hybrid approaches such as ANN–GA, ANN–PSO, and CNN-LSTM), training algorithms (back-propagation, Levenberg–Marquardt, Bayesian regularization), workpiece materials (AISI 52100, AISI H13, AISI D2, AISI 4340), cutting tool types (CBN, PCBN, coated carbides), and performance indicators (surface roughness Ra, cutting forces Fx/Fy/Fz, tool flank wear VB, cutting temperature). Key findings confirm that ANN models consistently outperform regression-based models in capturing non-linear input–output relationships, with prediction errors frequently below 5%. Recent trends toward hybrid ANN–genetic algorithm models, explainable machine learning (XAI), and real-time CNN-LSTM architectures signal a move toward intelligent and autonomous hard turning optimization systems.
Development of an AI-ML-Based Expert System for Extrusion-Based Manufacturing Processes: A Comprehensive Framework for Process Optimization, Defect Detection, and Real-Time Control
In this study, we developed and tested an artificial intelligence–machine learning based expert system called AIMES. The AIMES expert system was designed to be used in conjunction with extrusion-based manufacturing systems, including Fused Deposition Modeling (FDM), Single-Screw Polymer Extrusion Systems, and Hot-Melt Extrusion (HME) Systems. The system consists of three main modules: a knowledge acquisition module, a hybrid inference engine that utilizes both rule based systems and machine learning subsystems, and a real-time closed-loop feedback control interface. To evaluate the performance of the different machine learning algorithms included in the hybrid inference engine, four types of machine learning were tested. These include ANN (Artificial Neural Network), CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and XGBoost (Gradient Boosting Ensemble Learning Algorithm). Based upon these tests, it can be concluded that all four machine learning algorithms performed well, but the best-performing algorithm was XGBoost. We also tested AIMES using in-situ measurements and found that AIMES detected clogs in the nozzle before they caused damage or produced defective products 96.8% of the time. Furthermore, the testing revealed that AIMES detected clogs as quickly as 50 milliseconds after they occurred.
Friction and Wear Analysis of Nano-Particle-Reinforced Composite Ball Bearings for Aerospace Applications
A thorough study of the tribology of nano particle reinforced polymer composite ball bearings was carried out for an aerospace application. The three nano-composite formulations tested were; (i) PEEK matrix filled with alumina (Al₂O₃) nanoparticles (ii) PEEK matrix filled with silicon carbide (SiC) nanoparticles (iii) A hybrid Al₂O₃ / SiC loaded at 5wt %, 10wt % and 15wt %. Dry friction and wear testing were performed using a pin-on disc tribometer at contact pressures ranging from 0.5 MPa to 3.0 MPa and sliding speeds ranging from 0.5 m/s to 3.0 m/s. Worn surface characterization by SEM/EDX indicated that the hybrid loaded nano-composite formulation at 10wt% displayed the least friction coefficient (μ = 0.061) and minimum specific wear rate (Wₛ=1.8×10⁻¹⁴ m²/N), and represented reductions in these parameters of 43% and 57% compared to unreinforced PEEK. Thermally stable analysis demonstrated that the nano-composite material would be suitable for service temperatures up to 280°C. Therefore, it has been demonstrated that nano-reinforced PEEK composites can serve as light weight self-lubricating bearing materials for future generation of aerospace systems, such as satellite actuators and gas turbine auxiliary mechanisms.
The Renegotiation Trap: Political Economy Factors Driving Opportunistic Contract Modifications in Public-Private Partnerships
Public-Private Partnerships (PPPs), as an instrument of global infrastructure governance, have created a widespread issue among governments: the widespread renegotiation of PPP contracts. Renegotiations occur at a rate far greater than would be expected under normal circumstances; therefore, it can reasonably be concluded that most PPP renegotiations are opportunistic. This article examines why the renegotiation of PPPs occurs so frequently. The authors call this problem the “Renegotiation Trap,” because there exists a self-reinforcing cycle of political incentives, opportunistic bids, contract incompleteness, and lack of institutional oversight that makes renegotiating PPP contracts both possible and strategically rational. The authors draw upon three bodies of literature to explain why renegotiating PPPs occurs. First, incomplete contract theory is utilized to understand why renegotiating PPPs is necessary. Second, political economy frameworks are used to understand who benefits from the renegotiation of PPPs. Third, data collected from over 1,000 Latin American concession agreements are analyzed to identify when and how renegotiations occurred. Using these three approaches, the authors found that the primary causes of renegotiating PPPs were: (i) election cycles; (ii) soft budget constraints; (iii) bidder’s curse; (iv) information asymmetry; and (v) corruption. The authors also evaluate different reform options to improve the renegotiation process. These reform options include creating an independent regulatory body to oversee the renegotiation process, holding open auctions to determine the best price for services, requiring public disclosure of renegotiated terms, and providing additional funding sources for renegotiation. The authors conclude that improving the renegotiation process requires changing the political structure that provides the incentives for renegotiating PPP contracts rather than just changing the language contained within those contracts.
Comparative Study of AI-Based Optimization Techniques in VLSI Circuit Design Using Python
Successful design of very large scale integration (VLSI) circuits requires the use of various optimizations—power reduction, area, and time optimization. This requires the involvement of AI-based optimization techniques, such as machine learning (ML), deep learning (DL), genetic algorithms (GA), particle swarm optimization (PSO), and reinforcement learning (RL), to fulfill such tasks where traditional approaches fail to do so. The abundant libraries and ease of implementation have made Python a widely used platform in AI-based VLSI optimization. The methodology used in this study is a comparative study of different AI-based optimization algorithms used in the VLSI circuits. This research is structured in the following way: problem definition, data collection & preprocessing, implementation on Python, and performance evaluation. The best number was obtained using DL (CNN) and RL (95%) for the complex tasks (floorplanning and dynamic power management). ML (SVM) achieved high accuracy (90%) on well-defined problems but was not scalable for large designs. GA and PSO were good in performing global optimizations for problems like area minimization and routing but required fine-tuning of parameters. DL and RL had the highest computational complexity (GPU acceleration) and the lowest resource requirements, with easier implementation, with ML techniques. We found that DL and RL are better suited to large and complex designs and are slow for small and simple designs, whereas ML (SVM) and PSO are better suited to small and simple designs and are often slow given large and complex designs. Therefore, additional research is needed on the use of multiple AI techniques in a hybrid way to overcome limitations of the different techniques and enhance design efficiency. The results of this study can be applied in the following areas: AI-based Electronic Design Automation (EDA) tools, Power and performance optimization in semiconductor industries, Automated circuit design for advanced computing and development of Python-based AI stacks for VLSI research.