Given the rising global energy costs and strengthened environmental protection requirements, the optimization of motor system efficiency has become a crucial topic in industrial automation and transportation fields. This study explores the application of machine learning in enhancing the efficiency of motor drive systems. The primary objective of the research is to optimize operational parameters of motor drive systems using advanced machine learning techniques to improve efficiency and reduce operating costs. Different machine learning models, such as neural networks, decision trees, and support vector machines, are utilized in the research, and the Stacking ensemble learning method is further utilized to enhance the predictive accuracy and robustness of the models. The results demonstrate a significant reduction in system energy consumption after optimization, confirming the effectiveness of machine learning technologies in motor system efficiency management.