Induction motors constitute the operational backbone of contemporary industrial systems, and their unexpected failure can result in severe downtime, significant productivity loss, and substantial maintenance expenditure. Conventional maintenance approaches—primarily corrective and preventive—often fail to identify early-stage degradation, making them inadequate for modern industrial demands. To address this gap, the present study proposes an AI/ML-based predictive maintenance framework capable of real-time monitoring and automated fault diagnosis in three-phase induction motors. Multimodal sensor data, including vibration, temperature, current, and voltage signals, were acquired using an ESP32-based IoT architecture and subsequently analyzed through machine learning algorithms. Feature engineering incorporated Fast Fourier Transform (FFT) and statistical indicators such as Root Mean Square (RMS), kurtosis, and crest factor to enhance the quality of the diagnostic features. A Support Vector Machine (SVM) classifier was developed and achieved an accuracy of 95–98%, with a precision of 92%, recall of 95%, and an AUC of 0.97 for classifying faults such as bearing damage, rotor imbalance, and electrical irregularities. Additionally, MATLAB/Simulink-based vector control simulations validated the dynamic behavior of the motor under varying load and fault conditions. Overall, the integration of AI/ML with IoT demonstrates significant improvements in reliability, reducing unscheduled downtime by up to 25% and enhancing energy efficiency by 8–12%, thereby offering a scalable, Industry-4.0-ready solution for intelligent predictive maintenance.