The accurate prediction of slope stability is a challenging research endeavor, particularly in real-world environments. This study presents a machine learning (ML) model for evaluating slope stability that meets high precision and speed criteria in slope engineering. The goal of this study is to build an ensemble machine learning model that can accurately predict slope stability from both a classification and a regression point of view. We proposed here an ensemble bagging and boosting technique with appropriate base classifiers to substantiate the assertion. We improved the slope stability prediction models through random cross-validation by selecting seven quantitative parameters based on 125 data points. From a classification model perspective, the best slope prediction accuracy (>
90%) was attained by bagging with base classifier Decision Tree (DT), boosting with base classifier Random Forest (RF), and random forest with splitting criterion Gini-index. The ensemble classifier has attained an average enhancement of 8-10% in accuracy value compared to base classifiers. From the standpoint of a regression model, ensemble bagging regression enhances the average [Formula: see text] value by 8-10% relative to the conventional base regression models employed in this study. Six-dimensional reduction techniques were used to examine and illustrate potential data aggregation in the prediction of slope stability. We found that RF and ensemble bagging, when combined with the base classifier DT model, are the most superior and accurate models for predicting slope stability. We also found that these two models are robust in predicting slope stability, outperforming others with an average improvement of 6-8% in accuracy, even after reducing dimensions. The findings offer a fresh method for geotechnical engineering slope stability prediction. Experimental evaluation suggests that in terms of prediction accuracy, the ensemble bagging model is the most effective method for evaluating and predicting the stability of slopes.