Manufacturers need to know when a machine is about to fail to better plan for maintenance. For example, you might have a machine sensitive to temperature, velocity, or pressure changes. When these changes occur, they might indicate a failure. An IoT device is used to record the readings using optimal positioning. The ML model is built on the sensor data acquired from the IoT device for each type of machine component or sub-process to deal with the entire machinery system. This leads to an expansive set of ML models representing all of the critical machines in the manufacturing process and different types of predictions by acquiring real-time data from the sensors. Predictive maintenance can save considerable losses in any heavy industry by proactively saving the machines from failures by taking corrective measures.