Intelligent prediction of slope stability is a critical foundation for slope governance and slope structure design. Due to the complexity and nonlinearity of slope stability assessment, various intelligent prediction models often exhibit superior performance, offering new directions for addressing slope stability challenges. This study constructs a slope stability prediction hybrid model based on the Sine Cosine Algorithm (SCA) optimized Gradient Boosting Decision Tree (GBDT). A database was established using 436 slope cases, comprising 6 parameters (slope height H, slope angle B, unit weight y, cohesion C, internal friction angle ç, and pore water pressure coefficient r,) along with slope stability status data. The dataset was pided into an 80 % training set and a 20 % testing set. The hyperparameters of the modelwere tuned by integrating 5-fold cross-validation with the Sine Cosine Algorithm (SCA). The performance of the proposed method was evaluated using accuracy, precision, recall rate, Fr-score,  and AUC. Additionally, 5 classical classification machine learning models were compared to assess their applicability and predictive capabilities for slope stability. Results demonstrate that SCA significantly enhances the performance of the GBDT model, with the SCA-GBDT model achieving accuracy, precision, recall rate, FSm. and AUC of 0.886 0. 0.877, 0.915, 0.896, and 0.955, respectively. Characteristic importance analysis via the SHAP algorithm revealed that the internal friction angle has the most significant impact on prediction outcomes. The SCA-GBDT model provides a reliable method for slope stability prediction, applicable to practical slope engineering.