The Role of Machine Learning in Big Data Analytics: A Systematic Review - Shaurya Vir Singh Pathania (AG Volumes, 2025)
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.