This study investigated the prediction and optimization of residual stresses in stainless steel 316L components produced by Laser Powder Bed Fusion (LPBF). Combining experimental analysis, finite element simulation, and computational modeling, the research utilized a custom genetic algorithm (GA), multiple linear regression (MLR), and support vector machine (SVM) for optimization and prediction. Sixteen cylindrical specimens were manufactured, and residual stresses were measured via X-ray diffraction and calibrated through thermomechanical finite element simulations. ANOVA revealed that laser power, scan speed, hatch spacing, and especially layer height significantly influenced residual stress. The SVM model consistently outperformed both MLR and GA, effectively capturing the complex nonlinear relationships between process variables and residual stresses, despite its higher complexity. While MLR showed slightly higher predictive accuracy in some cases, and GA demonstrated competitiveness in capturing nonlinear interactions, SVM proved to be the most robust approach for modeling these inherent nonlinearities.