<p>Industrial automation has significantly increased the role of industrial robots in production. This research enhances real-time monitoring and fault prediction capabilities of industrial robots using machine learning. The study analyzes the principles of machine learning and the technical background of industrial robots. A series of experiments evaluates the performance of various machine learning models in fault prediction. Results show that, with proper data processing, model selection, and parameter optimization, an efficient and accurate prediction model can be achieved. Challenges like data quality and model generalizability are addressed through further optimization. This study improves fault prediction accuracy for industrial robots and serves as a reference for intelligent monitoring and maintenance in industrial automation.</p>