Industrial motors and rotating machines play a vital role in manufacturing and production environments, where unexpected failures can lead to significant downtime, financial loss and safety risks. Traditional maintenance approaches such as reactive maintenance, performed after a failure and preventive maintenance, based on fixed time intervals are inefficient and often fail to detect early- stage faults. Existing monitoring systems are expensive, complex and mostly suitable only for large-scale industries, making them inaccessible for small and medium enterprises. Hence a low-cost ESP32-based predictive maintenance system for real-time condition monitoring of industrial motors continuously monitors key health parameters such as vibration, temperature and current using multiple sensors. By analyzing these parameters in real time, the system can detect abnormal operating conditions at an early stage. Fault severity is classified into normal, warning and critical levels, which are indicated using visual LED alerts. In addition, the system provides a self-protection mechanism by automatically disconnecting the motor during severe fault conditions, preventing permanent damage.