Abstract: To enhance the prediction accuracy of data-driven models in slope stability classification, this study proposes a hybrid intelligent model (WMA-SVM) that integrates a novel Whale Migration Algorithm (WMA) with a Support Vector Machine (SVM). First, a heterogeneous dataset of slope cases from diverse engineering backgrounds was constructed. To address its significant class imbalance, a combined strategy using the Synthetic Minority Over-sampling Technique (SMOTE) and the Local Outlier Factor (LOF) algorithm was adopted to generate a high-quality balanced dataset. Subsequently, the WMA algorithm, which demonstrated superior optimization performance on eight benchmark test functions, was employed to optimize the hyperparameters of the SVM adaptively. Evaluation results show that the proposed WMA-SVM model significantly outperforms all benchmark models across various performance metrics. Moreover, based on the Permutation Feature Importance (PFI) method, the unit weight (γ), slope angle (β), and internal friction angle (φ) were identified as the most critical features influencing the classification outcomes for this dataset. Finally, the model's generalization capability was further validated through eight independent engineering case studies, revealing a high consistency between the predictions and the actual stability states. This research provides a modeling framework with considerable generalization potential for the intelligent analysis of slope stability.