This study examines the effects of mechanical behavior, thermal characteristics, and tribological variables (sliding frequency, normal load, and temperature) on the tribological performance of CNTs-coated aramid fabric-reinforced epoxy composites using a computational and data-driven machine learning (ML) approach. Predictive models for the coefficient of friction (COF) were developed based on previous tribological, mechanical, and thermal data, employing three ML algorithms: Artificial neural network (ANN), Gradient Boosting Machines (GBM), and Random Forest (RF). The models showed that ANN achieved an (R2 = 0.9088), GBM (R2 = 0.92807), and RF (R2 = 0.85294) with the GBM model providing the best predictions. The dataset with the best performance had an error percentage of 0.003658%, while the poorest performance showed 13.56625%. Feature score analysis highlighted load, sliding frequency, and CNTs content as key factors influencing COF. This data-driven ML analysis offers significant insights into the tribological behavior of fiber-reinforced polymer composites, aiding in material design and performance optimization.