Ensuring consistent electricity supply to consumers necessitates a reliable composite power system. Traditional methods struggle to manage the complexity of modern power systems, underscoring the need for advanced data mining approaches like machine learning (ML). This research evaluates the performance of various ML techniques, including K-Nearest Neighbor (KNN), Linear Classifier (LC), Ensemble methods (EN), and Neural Networks (NN), in assessing the reliability of composite power systems. Numerical simulations on IEEE RTS 79 and 96 test systems demonstrate that while EN exhibits perfect accuracy, other ML techniques achieve comparable results. However, computational efficiency varies significantly among these techniques. LC, in particular, outperforms others in terms of computational speed, making it a promising choice for real-time reliability assessment. This study provides valuable insights into the strengths and limitations of different ML techniques for reliability evaluation, guiding the selection of appropriate methods for diverse power system applications