The emergence of Industry 4.0 has brought a data-driven revolution to manufacturing and industrial processes, where interconnected devices, sensors, and systems continuously generate massive amounts of data. Predictive maintenance, powered by big data analytics, plays a critical role in this new industrial paradigm by enabling companies to forecast equipment failures, minimize downtime, and optimize maintenance schedules. This research explores the application of big data techniques—such as machine learning algorithms, anomaly detection, and time-series analysis—to process and Analyse IoT-generated data from industrial machinery. By detecting patterns and trends in equipment performance, predictive models can be developed to anticipate malfunctions before they occur, significantly reducing unplanned outages and repair costs. The study will focus on integrating big data platforms with real-time monitoring systems to create scalable predictive maintenance frameworks. Case studies will be Analysed to demonstrate the economic benefits, including extended equipment lifespan, reduced operational disruptions, and enhanced production efficiency. The research also addresses the challenges of data integration, system interoperability, and the role of edge computing in facilitating real-time predictive analytics in distributed industrial environments.