Slope stability analysis is regarded as a critical issue in geotechnical engineering. However, inherent limitations exist in traditional evaluation methods, and complex nonlinear relationships among influencing factors cannot be fully characterized. Therefore, an intelligent prediction model for slope stability based on the integration of multiple optimization algorithms is proposed to improve prediction accuracy and model interpretability. First, eight widely used machine learning models were compared, and the random forest model was selected as the optimal baseline model. Then, its hyperparameters were collaboratively optimized using the Lung Performance Optimization algorithm, the Golden Sine Algorithm, and the Slime Mould Algorithm to further enhance predictive performance. Subsequently, an ensemble learning strategy was adopted, and the corresponding hyperparameters were optimized using a successive halving grid search approach. The ensemble model was compared with individual models, and it is indicated that superior performance in both accuracy and stability is achieved. To improve model interpretability, the SHAP and LIME methods are employed to quantify feature contributions to prediction results and to identify key influencing factors and their relative importance. It is shown that the optimized random forest model achieves an accuracy of 0.9359, while better performance is achieved by the ensemble model, with an accuracy of 0.9487 and favorable F1-score and precision. Finally, a software system integrating data visualization and slope stability prediction functions is developed. Efficient and reliable support is provided by the system for intelligent slope stability evaluation.