This project presents an IoT-based predictive maintenance system based on machine learning algorithms—Random Forest, Logistic Regression, SVM, and LSTM—to identify motor faults precisely. Real-time data such as sound, vibration, and RPM are recorded through hardware prototyping, while Simulink simulates speed and torque. Data is transmitted to Firebase for real-time monitoring, prompting automated fault notifications. This system improves industrial efficiency by minimizing sudden failures, reducing maintenance expenses, and increasing machinery lifespan through prompt, data-driven interventions.