Abstract: Interfacial fretting fatigue in high-strength bolted friction-type connections constitutes a critical limiting factor affecting their long-term performance and structural safety. To enable accurate prediction of service life, this study first establishes a theoretical solution for the mechanical response within the fretting contact region of the interface by employing the modified Hertz contact theory and the Boussinesq-Cerruti displacement solution. Subsequently, accounting for the wear-fatigue coupling effect, a damage parameter correction model is proposed by incorporating relative slip displacement and surface roughness parameters, thereby improving the prediction accuracy of the SWT and KBM models—with an average increase of approximately 20% in the proportion of predictions falling within a 1.5-fold error band. Finally, both a purely data-driven PSO-BP neural network model and a data-knowledge-integrated neural network model are developed to predict the fretting fatigue life under multi-factor coupling conditions. Results demonstrate that the proposed data- and knowledge-driven approach achieves high predictive accuracy, with 95.2% of predictions from the integrated model in the test set lying within the 2-fold error band. This work presents a novel framework for evaluating the fretting fatigue performance of bolted connections under complex and coupled influencing factors.