This study presented a procedure for predicting slope stability using four machine learning algorithms (extreme gradient boosting, support vector machine, logistic regression, and random forest). Based on a real-case database of 168 multinational slopes, this study analyzed the impact of six influential inputs: slope angle, friction angle, cohesion, height, pore pressure ratio, and unit weight. Notably, the results showed that the extreme gradient boosting algorithm surpassed other algorithms, achieving approximately 94% accuracy in predicting real-case slopes. The final validation tests, using 189 datasets (including 168 real cases and 21 additional slopes), revealed that the proposed XGBoost model achieved 93.75% accuracy, 96% precision, 92.3% recall, and 94.1% F1 score, suggesting a robust classifier with minimal misclassification, particularly in differentiating between stable and unstable slopes. The results also demonstrated that soil unit weight is the most significant parameter in slope stability analysis.
