Failure of tailings dams is a major issue in the mining industry as it critically impacts the environment and life. A major cause of the failure of tailings dams is the unplanned depositing of tailings and the increase in saturation due to rainfall events. This study using numerical modelling and artificial intelligence techniques (like MLR, SVR, DT, RF, and XGB) aims to predict the slope stability of tailings dams to avoid failure. The stability of tailings dams is analysed using the finite difference method (FDM), which computes the factor of safety (FoS) using the shear strength reduction (SSR) technique. This investigation mainly focuses on the geotechnical and geometric parameters of the tailings dam, such as density, cohesion, friction angle, saturation, embankment height, slope angle and haul road width. Results of numerical modelling have been used for developing ML models and predicting slope stability. The efficiency of ML models was analysed based on the R2 and root mean square error (RMSE), mean squared errors (MSE), and mean absolute error (MAE). The XGB algorithm proved to be the most effective as it gave the highest accuracy and lowest RMSE value compared to other ML models. AI tool was developed based on the ML model results for dam slope stability prediction. The developed AI tool will help understand the role of saturation and geometry parameters in embankment stability at the initial level of investigation.