Machine Learning for Big Data Analytics: Challenges, Trends, and Future Research Directions

The rapid expansion of Big Data has revolutionized how organizations and societies create, store, and use information, but it has brought opportunities and challenges to effective intelligent data-driven decision-making. The capacity of Machine Learning (ML) to learn patterns, make predictions, and automate processes has turned it into an important key to unlocking the full potential of Big Data. This survey gives an in-depth overview of the overlap between ML and Big Data, including fundamental concepts, technology ecosystems, and a wide range of applications across the areas of healthcare, finance, smart cities, and governance. It also looks at some of the main issues like data heterogeneity, scalability, interpretability, security and privacy issues that restrict large-scale adoption. The research identifies some of the recent developments in the field such as deep learning, federated learning and edge intelligence which are transforming Big Data analytics. In addition, the paper outlines new research frontiers like sustainable and energy-efficient ML, hybrid models of heterogeneous data, human-in-the-loop systems, and cross-disciplinary applications to solve social issues such as climate change and disease epidemiology. Through a synthesis of previous work, this survey highlights the paradigm-shifting potential of ML in deriving actionable intelligence out of Big Data and describes future directions in developing scalable, secure, interpretable, and socially responsible analytics systems.