Laser powder bed fusion (LPBF) has emerged as a promising additive manufacturing technique for producing high-performance 304L austenitic stainless steel, which is widely used in the aerospace and automotive industries and in biomedical engineering because of its excellent mechanical properties, corrosion resistance, and high-temperature stability. However, the mechanical properties of LPBF-fabricated materials are influenced by the complex interplay of various process parameters, including laser power, scanning speed, layer thickness, and hatch spacing. Traditional trial-and-error methods for process optimization are costly and time-consuming, and they often fail to precisely control the material's microstructure, resulting in suboptimal performance. Therefore, there is a pressing need to adopt advanced approaches to systematically optimize the LPBF process and ensure reliable, high-performance outcomes. This study proposes an innovative hybrid intelligent framework that integrates Stacking-ensemble learning, interpretable machine learning techniques (Shapley additive explanation (SHAP) method), and multiobjective optimization (nondominated sorting genetic algorithm II-technique for order preference by similarity to ideal solution (NSGA-II-TOPSIS)). The primary objective is to develop an all-encompassing framework for the simultaneous prediction and optimization of ultimate tensile strength, yield strength, and elongation in LPBF-fabricated 304L stainless steel. The framework is designed not only to improve the accuracy of predicting the mechanical properties but also to provide a clear understanding of the influence of process parameters on material behavior. The Stacking-ensemble model demonstrates superior performance in terms of accuracy, generalization, and stability compared to individual machine learning models, such as random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). SHAP analysis has revealed that laser power plays a critical role in determining the mechanical properties of the material, making it the most important factor to consider in the optimization process. The multiobjective optimization approach facilitates the identification of the optimal process parameters, resulting in a balanced strength-ductility trade-off that is crucial for practical applications. Experimental validation was conducted to confirm the effectiveness of the proposed framework. The optimized LPBF samples exhibited refined, uniform cellular substructures, increased dislocation density, and the formation of twin boundaries, which significantly improved the material's mechanical properties.