Industrial device monitoring and predictive maintenance are becoming crucial aspects of industries relying on heavy machinery and equipment. Predictive maintenance, in particular, has emerged as a critical approach to optimizing maintenance strategies and minimizing costly equipment failures. By leveraging IoT sensors and machine learning on the edge, the landscape of predictive maintenance has undergone significant transformation. This shift allows for real-time data collection, analysis, and decision-making at the edge, enabling faster response times and proactive maintenance actions. Unlike traditional predictive maintenance techniques, which often rely on periodic inspections or time-based schedules, this new framework takes advantage of continuous monitoring, anomaly detection, and predictive analytics to anticipate and even prevent equipment failures, resulting in increased operational efficiency, reduced downtime, and significant cost savings.