Abstract: The reliability analysis of slopes is often hindered by time-consuming or challenging evaluations of large deformation, particularly when dealing with the intricate effects of spatial variability in soil parameters on large deformations. To overcome these obstacles, this study introduces an innovative methodology that seamlessly integrates t-distributed stochastic neighbor embedding (t-SNE), active learning multiple adaptive regression spline (AMARS), and the material point method (MPM). At the core of this novel approach, Cholesky decomposition serves as a crucial tool for discretizing the complex random fields of slope parameters, thereby facilitating subsequent deterministic analysis to generate essential training samples. These samples serve as the foundation upon which the MARS model is constructed and further refined through employing an active learning function and construct AMARS model to ensure optimization and adaptability. Subsequently, leveraging Monte Carlo simulation (MCS), augmented by AMARS model delivers reliable estimates of slope stability. This integration provides a robust framework for quantifying uncertainties and predicting the likelihood of slope failures under varying conditions. In order to gain deeper insights into failure mechanisms, meticulous examination using material point method (MPM) is employed to analyze failure samples and unravel intricate dynamic evolution processes associated with diverse failure modes. This comprehensive analysis, demonstrated through a two-layer cohesive soil slope example not only enhances our theoretical understanding but also offers practical insights for real-world applications. Remarkably, results demonstrate that our proposed approach significantly outperforms random material point method (RMPM) with an impressive computational cost reduction rate of 1.64%. It is notably noteworthy that the multi-layer progressive failure mode, being the most intricate and complex of all failure processes, poses a significantly considerable threat to the adjacent environment, thereby necessitating urgent and heightened attention to ensure safety and mitigate potential hazards. This comprehensive study provides a crucial basis for the rigorous assessment and reinforcement of slope stability risks.