Modern manufacturing lines depend heavily on electronic systems to keep operations running smoothly. But when these systems fail unexpectedly, the consequences can be costly. This paper introduces a new predictive maintenance approach using a carefully designed deep learning system that brings together multiple types of models: convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and autoencoders. These models work together to analyze different kinds of sensor data thermal images, vibrations, electrical current, and sound. The system anticipates equipment failures with an accuracy above 94%, giving operators almost 2 days’ notice before problems occur. Compared to traditional methods, this can cut maintenance costs by 35%. This paper explains how the system works, provides detailed technical and mathematical foundations, and shows how it performs in real industrial environments.