The reliability of Direct Current (DC) motors is critical to industrial productivity, yet mechanical components such as commutators and brushes are highly susceptible to wear and failure. This paper presents a rigorous comparative analysis of machine learning-based Fault Detection and Diagnosis (FDD) frameworks for Permanent Magnet DC (PMDC) motors. Specifically, we evaluate and compare the diagnostic performance of K-Nearest Neighbors (KNNs), Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks in classifying three operational states: Normal, Brush-Wear, and Commutator-Fault. Utilizing an open-source industrial dataset, each model was optimized to distinguish between subtle fault signatures that often lead to unplanned downtime. Our experimental results demonstrate a clear performance hierarchy: the baseline KNN model achieved an accuracy of 93.3% but was vulnerable to overlapping feature spaces, whereas the ANN improved accuracy to 94.7% by capturing non-linear relationships. The proposed LSTM architecture significantly outperformed both models, achieving a superior validation accuracy of 97.4% and near-perfect precision for normal and brush-wear conditions. This superior performance is attributed to the LSTM’s specialized gating mechanisms, which effectively capture long-term temporal dependencies within motor current and vibration signals. The study concludes that temporal deep learning models offer the most robust solution for automated predictive maintenance in complex industrial environments.