This paper reviews the development and the advancements which have been made in the intelligent predictive maintenance system, which uses the Internet of Things to improve machine reliability and optimize the management schedules in maintenance. Sensors play a vital role in IoT as it incorporates machines used in terms of monitoring and controlling fundamental machine parameters such as temperature, vibration, and pressure, which provide real-time data analysis. This paper discusses machine learning algorithms, clustering techniques, and other data analysis methods in anomaly detection and the prognosis of potential equipment failures. In these systems, some of the principal stages include data collection; real-time streaming; data preprocessing; and anomaly detection. Further on, the paper addresses some challenges such as integrating sensor data coming from heterogeneous sources, the real-time nature required for their processing, and large industrial-scale scaling. This review states the increased adoption of IoT-driven predictive maintenance and its potential for industrial operations to change. It is really all about reducing downtime in industries and improving efficiency.