Heavy Industries employ IoT and ML-based predictive maintenance. Protect equipment health with real-time measurements collected by sensors attached directly to the equipment. ML-based approaches process these high-resolution data sets to predict failures before they occur and schedule maintenance to minimize downtime. The primary components of predictive maintenance systems are anomaly detection models (e.g., CNNs and RNNs), which utilize historical and real-time measurements to identify early signs of impending malfunctions. The prediction of remaining useful life (RUL) is critical for accurate maintenance actions. Raw data processing occurs at the source itself, effectively reducing latency and preventing bandwidth from becoming a bottleneck. This integration serves not to extend the life of the devices but rather allows for general cost savings through limited emergency maintenance and optimal resource allocation. Additionally, it enhances decision-making through actionable insights. It promotes an initiative-taking maintenance culture, addressing data security, the complexity of IoT networks, and the heterogeneity of industrial environments to support scalable models. The early preventive maintenance methods resulted in a reduction in spare parts deliveries and downtime but relied on tedious data acquisition and the intervention of consultants.