The Role of Machine Learning in Big Data Analytics: A Systematic Review
Keywords:
Healthcare, Finance, Supply Chain Management, Fraud Detection, Risk Management and Decision-MakingAbstract
The exponential growth of digital data has created a demand on sophisticated analytical methods that can help to derive useful information in complicated and large volumes of data. Machine Learning (ML) has become the key facilitator of Big Data Analytics (BDA) and it presents scalable, adaptive, and data-driven solutions to various fields. This literature review focuses on how ML techniques, such as regression models, decision trees, ensemble, support vector machines and neural networks, can be used to improve predictive accuracy, anomaly detection, decision support, and real-time analytics. The paper summarizes the latest research that demonstrates its implementation in the healthcare domain, finance, supply chain management, Industry 4.0, and business intelligence. Although, ML driven BDA significantly enhances operational efficiency and decision-making, there are still issues, including data quality, scalability, security, ethical issues, and skills crunch. The review highlights gaps in the research and the necessity of cost-effective, scaleable and domain-specific ML frameworks in order to utilize the transformative potential of big data analytics fully.
References
[1] P. Rajeswari, V. E. Sathishkumar, C. Anilkumar, P. Thilakaveni, and U. Moorthy, “Big Data Analytics and Implementation Challenges of Machine Learning in Big data,” Int. Conf. Comput. Data Sci., pp. 532–537, 2022, doi: 10.54254/2755-2721/2/20220584.
[2] E. Dritsas and M. Trigka, “Exploring the Intersection of Machine Learning and Big Data: A Survey,” Mach. Learn. Knowlidge Extr., vol. 7, no. 13, pp. 1–42, 2025.
[3] S. Joshi, “Review of Gen AI Models for Financial Risk Management: Architectural Frameworks and Implementation Strategies,” Int. J. Innov. Sci. Eng. Manag., vol. 4, no. 3, pp. 207–222, 2025, doi: 10.69968/ijisem.2025v4i2207-222.
[4] J. Kumari, E. Kumar, and D. Kumar, “A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics,” Arch. Comput. Methods Eng., vol. 30, no. 6, pp. 3673–3701, 2023, doi: 10.1007/s11831-023-09915-y.
[5] C. I. Eke et al., “Machine Learning Based Mobile Big Data Analytics: State-of-the-art Applications, Taxonomy, Challenges and Future Research Directions,” Niger. J. Technol. Dev., vol. 22, no. 4, pp. 65–89, 2025.
[6] P. Dixit and P. B. Singh, “The Impact of Artificial Intelligence on Various Sectors: Benefits, Challenges, and Future Prospects,” Interantional J. Sci. Res. Eng. Manag., vol. 08, no. 04, pp. 140–144, 2024, doi: 10.69968/ijisem.2024v3si2140-144.
[7] R. Almutiri, S. Alhabeeb, S. Alhumud, and R. U. Khan, “A Survey of Machine Learning for Big Data Processing,” J. Big Data, vol. 4, no. 2, pp. 97–111, 2022, doi: 10.32604/jbd.2022.028363.
[8] S. S. Kona, “Harnessing AI, Machine Learning, and Big Data for Next-Generation Analytics across Industries,” J. Artif. Intell. Cloud Comput., vol. 3, no. 1, pp. 1–5, 2024.
[9] A. Alam et al., “Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review,” J. Med. INTERNET Res., vol. 26, no. e54710, pp. 1–27, 2024, doi: 10.2196/54710.
[10] F. M. Surur et al., “Unlocking the power of machine learning in big data: a scoping survey,” Data Sci. Manag., vol. 8, no. 4, pp. 519–535, 2025, doi: 10.1016/j.dsm.2025.02.004.
[11] K. Taha, “Machine learning in biomedical and health big data: a comprehensive survey with empirical and experimental insights,” J. Big Data, vol. 12, no. 61, pp. 1–39, 2025, doi: 10.1186/s40537-025-01108-7.
[12] M. M. Rathore, S. A. Shah, D. Shukla, E. BENTAFAT, and S. BAKIRAS, “The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities,” IEEE Access, vol. 9, pp. 32030–32052, 2021, doi: 10.1109/ACCESS.2021.3060863.
[13] A. H. Gandomi, F. Chen, and L. Abualigah, “Machine Learning Technologies for Big Data Analytics,” Electronics, vol. 11, no. 421, pp. 2–5, 2022.
[14] N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Machine Learning and Deep Learning for Big Data Analytics: A Review of Methods and Applications,” Partners Univers. Int. Innov. J., vol. 2, no. 3, pp. 172–197, 2024, doi: 10.5281/zenodo.12271006.
[15] I. K. Nti, J. A. Quarcoo, J. Aning, and G. K. Fosu, “A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects,” BIG DATA Min. Anal., vol. 5, no. 2, pp. 81–97, 2022, doi: 10.26599/BDMA.2021.9020028.
[16] A. S. Mavai, D. K. Mishra, A. Sharma, and S. Bansal, “Role of big data analysis to improve the efficiency of healthcare system using machine learning techniques,” Discov. Internet Things, vol. 5, no. 153, pp. 1–37, 2025.
[17] N. Prawira, Wella, and F. Natalia, “Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review,” Int. J. INFORMATICS Vis., vol. 9, no. 4, pp. 1783–1791, 2025.
[18] G. P. Kumar, “TRANSFORMATION OF BIG DATA THROUGH MACHINE LEARNING AND IT’S APPLICATIONS,” Anveshana’s Int. J. Res. Eng. Appl. Sci., vol. 9, no. 10, pp. 16–26, 2024.
[19] S. K. Nanjappan, “Machine Learning in Data Analytics and Reporting: Advancing Decision-Making Processes in the Digital Age,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 12, no. IX, pp. 1193–1200, 2024.
[20] C. V. Shaw, “Hybrid Approaches to Big Data Analytics Using Machine Learning Techniques,” Kron. J., vol. 24, no. 8, pp. 12–30, 2024.
[21] E. Barzizza, N. Biasetton, R. Ceccato, and L. Salmaso, “Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review,” Stats, vol. 6, pp. 596–616, 2023.
[22] D. Gupta and S. Gupta, “Big Data Analysis using Machine Learning for Health Systems,” Int. J. Comput. Appl., vol. 185, no. 9, pp. 8–13, 2023, doi: 10.5120/ijca2023922745.
[23] S. H. Akundi and M. P. Soujanya R, “Big Data Analytics in Healthcare Using Machine Learning Algorithms: A Comparative Study,” iJOE, vol. 16, no. 13, pp. 19–32, 2020
