The long-term reliability of stainless steel-316 (SS-316) components in high-temperature environments is a critical consideration in sectors such as energy production and aerospace engineering. In particular, welded or bonded joints in SS-316 structures are often the most vulnerable to creep deformation due to localized stress concentration and thermal exposure. This study advances the predictive capability of a recently developed creep model for SS-316 joints by incorporating artificial intelligence (AI)–based parameter optimization and machine learning (ML)–driven residual correction. Experimental creep data obtained from SS-316 joint specimens under varied stress and temperature conditions formed the basis for model calibration. Parameter refinement was carried out using Particle Swarm Optimization and Genetic Algorithms, both of which effectively reduced systematic prediction errors. Complementary ML models, including Support Vector Regression and Gradient Boosted Trees, were trained to identify and model complex nonlinear patterns that the analytical approach alone could not capture. Model accuracy was quantified using metrics such as the mean absolute percentage error (MAPE) and the coefficient of determination (R²). The optimized model exhibited a reduction in MAPE exceeding 25% compared to its unoptimized counterpart, while the hybrid analytical–ML framework achieved an R² of 0.98 on the validation dataset. These results confirm that integrating AI-driven optimization with ML-based correction significantly improves predictive accuracy and generalization for SS-316 joint creep behavior. The proposed approach not only enhances the modeling of high-temperature joint performance but also offers a transferable methodology for other materials and joint configurations subjected to complex thermo-mechanical loads, thereby contributing to safer and more efficient engineering design.