A Comprehensive Literature Review on the Role of Machine Learning Across Domains: Advances, Applications, and Future Directions
Keywords:
Artificial Intelligence, Behavioral Economics, Machine Learning, VANET.Abstract
Machine Learning (ML) has become one of the most disruptive technologies in such fields as finance, healthcare, disaster management, big data analytics, intelligent transportation systems, behavioral economics, radiomics, and human-computer interaction. The literature review is based on over forty peer-reviewed publications and provides one of the unified views of applications, advancements, challenges, and emergent trends of ML. Improvements in deep learning oil, generative AI, anomaly detection, supervised and unsupervised learning, multimodal learning, GAN-based data generation, ethical AI, and optimization methods are also covered in the review. This review shows the impact of machine learning on industries, positioning it as a predictive analytics tool, decision-making tools, automation, and real-time intelligent systems and highlights it through compiling studies on how machine learning is transforming industries published in 2017 to 2025. Some of the critical technical problems such as interpretability, fairness, data quality, scalability and real time deployment are also discussed. The research directions are outlined in the future in the context of explainable ML, hybrid AI systems, integrated multimodal architectures, and responsible AI frameworks.
Downloads
References
1. Abhilash, D., Chandrashekar, C., & Shalini, S. (2017, December). Economical, energy efficient and portable home security system based on Raspberry Pi 3 using the concepts of OpenCV and MIME. In 2017 International Conference on Circuits, Controls, and Communications (CCUBE) (pp. 60-64). IEEE.
2. Amudala Puchakayala, P. R., Sthanam, V. L., Nakhmani, A., Chaudhary, M. F., Kizhakke Puliyakote, A., Reinhardt, J. M., & Bodduluri, S. (2023). Radiomics for improved detection of chronic obstructive pulmonary disease in low-dose and standard-dose chest CT scans. Radiology, 307(5), e222998.
3. Bansal, A., Puchakayala, P. R. A., Suddala, S., Bansal, R., & Singhal, A. (2025, May). Missing Value Imputation using Spatio-Convolutional Generative Adversarial Imputation Network. In 2025 3rd International Conference on Data Science and Information System (ICDSIS) (pp. 1-6). IEEE.
4. Barve, A., Pallavi, R., Deepak, S., Murugan, R., Yadav, D., Singh, A. K., & Shalini, S. (2024). A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs). International Journal of Information Technology, 16(3), 1429-1435.
5. Ghori, P. (2018). Anomaly detection in financial data using deep learning models. International Journal Of Engineering Sciences & Research Technology, 7(11), 192-203.
6. Ghori, P. (2019). Advancements in Machine Learning Techniques for Multivariate Time Series Forecasting in Electricity Demand. International Journal of New Practices in Management and Engineering, 8(01), 25-37. Retrieved from https://ijnpme.org/index.php/IJNPME/article/view/220
7. Ghori, P. (2021). Enhancing disaster management in India through artificial intelligence: A strategic approach. International Journal of Engineering Sciences & Research Technology, 10(10), 40–54.
8. Ghori, P. (2021). Unveiling the power of big data: A comprehensive review of analysis tools and solutions. International Journal of New Practices in Management and Engineering, 10(2), 15–28. https://ijnpme.org/index.php/IJNPME/article/view/222
9. Ghori, P. (2023). LLM-based fraud detection in financial transactions: A defense framework against adversarial attacks. International Journal of Engineering Sciences & Research Technology, 12(11), 42–50.
10. Ghule, P. A. (2025). AI in Behavioral Economics and Decision-Making Analysis. Journal For Research In Applied Sciences And Biotechnology, Учредители: Stallion Publication, 4(1), 124-31.
11. Ghule, P. A., Sardesai, S., & Walhekar, R. (2024, February). An Extensive Investigation of Supervised Machine Learning (SML) Procedures Aimed at Learners’ Performance Forecast with Learning Analytics. In International Conference on Current Advancements in Machine Learning (pp. 63-81). Cham: Springer Nature Switzerland.
12. Marathe, S., Arosh, S., & Mondal, T. (2022, December). A CNN-inspired reverse search engine for content-based image retrieval. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 996-1001). IEEE.
13. Puchakayala, P. R. A. (2022). Responsible AI Ensuring Ethical, Transparent, and Accountable Artificial Intelligence Systems. Journal of Computational Analysis and Applications, 30(1).
14. Puchakayala, P. R. A. (2024). Generative Artificial Intelligence Applications in Banking and Finance Sector. Master's thesis, University of California, Berkeley, CA, USA.
15. Ravindranath, R. C., Vikas, K. R., Chandramma, R., Sheela, S., & Dhiraj, C. (2025, June). DermaGAN: Enhancing Skin Lesion Classification with Generative Adversarial Networks. In 2025 International Conference on Emerging Technologies in Computing and Communication (ETCC) (pp. 1-8). IEEE.
16. Saha, P., Bodduluri, S., Nakhmani, A., Chaudhary, M. F., Amudala Puchakayala, P. R., Sthanam, V., & Bhatt, S. P. (2025). Computed tomography radiomics features predict change in lung density and rate of emphysema progression. Annals of the American Thoracic Society, 22(1), 83-92.
17. Sardesai, S., & Gedam, R. (2025, February). Hybrid EEG Signal Processing Framework for Driver Drowsiness Detection Using QWT, EMD, and Bayesian Optimized SVM. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-6). IEEE.
18. Shalini, S., & Patil, A. P. (2021). Obstacle-Aware Radio Propagation and Environmental Model for Hybrid Vehicular Ad hoc Network. In Inventive Computation and Information Technologies: Proceedings of ICICIT 2020 (pp. 513-528). Singapore: Springer Nature Singapore.
19. Shalini, S., Gupta, A. K., Adavala, K. M., Siddiqui, A. T., Shinkre, R., Deshpande, P. P., & Pareek, M. (2024). Evolutionary strategies for parameter optimization in deep learning models. International Journal of Intelligent Systems and Applications in Engineering, 12(2S), 371–378.
20. Sheela, S., & Shalini, S. (2024). Prediction of cardiac disabilities in diabetic patients. In Futuristic trends in network & communication technologies (IIP Series, Vol. 3, Book 4, Part 2, Chapter 2, pp. 123–129). Integrated Intelligent Publication.
21. Sheela, S., Nataraj, K. R., & Mallikarjunaswamy, S. (2023). A comprehensive exploration of resource allocation strategies within vehicle Ad-Hoc Networks. Mechatron. Intell. Transp. Syst, 2(3), 169-190.
22. Sheela, S., Nataraj, K. R., & Rekha, K. R. (2022, November). Design and Implementation of Hand Gesture for Various Applications. In Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 1 (pp. 721-728). Singapore: Springer Nature Singapore.
