Abstract: By integrating Rhee's wear theory with a customized fluid-solid coupling model tailored for reciprocating seals, a predictive model was developed, providing profound insights into the wear behavior of these seals. This model facilitated an exhaustive analysis of six distinct T-ring configurations, each characterized by unique features and operational parameters, resulting in a detailed dataset mapping wear progression across these configurations. To enhance the predictive accuracy and refine the model, advanced Deep Neural Network (DNN) technology was incorporated. Through meticulous hyperparameter optimization, a sophisticated wear profile prediction model was established, effectively capturing the complex interplay of factors influencing wear behavior. Experimental validation confirmed the robustness of the DNN-based model, exhibiting remarkable performance with prediction accuracy exceeding 90% and a recall rate surpassing 95%. These exceptional results highlighted the substantial potential of our model as a pivotal tool for anticipating wear profiles in reciprocating seal systems. Overall, this research constituted a significant advancement in tribology and mechanical engineering, contributing novel insights and methodologies for the design, optimization, and maintenance of reciprocating seal systems.