This study investigates the self-lubricating properties of epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) functionalized with carboxyl (COOH), amine (NH 2), and silane coupling agents. Advanced artificial intelligence (AI) methods were employed to predict key tribological parameters coefficient of friction (COF) and wear rate as well as to classify wear mechanisms. Wear tests were conducted using a linear reciprocating tribometer under dry sliding conditions with a chromium steel ball as the counter surface. To simulate demanding conditions beyond typical industrial polymer bearings, sliding frequencies of 2, 5, and 8 Hz were applied under normal loads of 5, 10, and 15 N. The resulting data were analyzed using AI tools to identify patterns and predict tribological performance. An Artificial Neural Network (ANN) was developed to model the relationships between COF, wear rate, experimental conditions, mechanical properties, and MWCNT composition, achieving high predictive accuracy (R 2 = 0.98 for COF; R 2 = 0.78 for wear rate). A Recurrent Neural Network (RNN) was used to capture the temporal evolution of COF under dominant wear mechanisms, including abrasion, adhesion, fatigue, and severe delamination. Additionally, a Convolutional Neural Network (CNN) accurately classified wear mechanisms from scanning electron microscopy (SEM) images. These AI-driven approaches provide a robust predictive framework for understanding and optimizing the tribological behavior of functionalized MWCNT-epoxy composites.