Abstract: Molding shrinkage significantly impacts the precision of selective laser sintering (SLS) components, with process parameters playing a critical role in material sintering and shrinkage deformation. Enhancing molding performance quality holds paramount importance. To curtail testing costs in the optimization of process parameters for SLS-molded parts, we introduce the CSO-LSSVM molding accuracy prediction model. The model’s design rationale is multifaceted: first, we comprehensively enhance the convergence accuracy and optimization speed of the snake optimizer (SO) through a trio of improvement strategies—Sine mapping, nonlinear switching factors, and pinhole imaging reverse learning. Subsequently, we seamlessly integrate the termed chaotic multi-strategy enhanced snake optimizer (CSO) with the least square support vector machine (LSSVM) to fine-tune pivotal kernel function parameters, thereby augmenting predictive accuracy and generalization capabilities. To affirm the validity and superiority of the CSO-LSSVM model, we leverage Matlab software for comparative analysis against LSSVM, BP (back propagation) neural network, and extreme learning machine (ELM) models using authentic datasets. Results unequivocally establish the method's heightened predictive accuracy, substantiated by error evaluation metrics: root mean square error of 0.546 2, mean absolute percentage error of 9.487 7, and mean absolute error of 0.401 7, respectively. This model facilitates the provisioning of optimal process parameters for SLS molding, thereby offering effective processing guidance.