<p>Slope stability analysis is crucial to the safety of geotechnical engineering. However, traditional methods (such as the limit equilibrium method and finite element method) are constrained by issues like simplified assumptions, strong parameter dependence, and insufficient dynamic adaptability, which hinders their ability to meet the analysis needs under complex geological conditions. With the advancement of artificial intelligence technology, data-driven deep learning has emerged as a research frontier in this field. Leveraging its strength in efficiently tackling complex nonlinear problems, it provides a new pathway to overcome the limitations of traditional methods. This paper presents a systematic review and critical evaluation of the application of deep learning in slope stability analysis, focusing on its application scenarios and challenges. Based on bibliometric analysis and tracking of technological evolution, the application of deep learning in slope engineering is found to exhibit three-stage developmental characteristics. The study examines the performance of five mainstream models across various classification tasks (such as safety factor prediction, deformation and displacement analysis, and landslide susceptibility analysis) as well as in practical applications. It also highlights that hybrid models integrated with intelligent optimization algorithms (e.g., Particle Swarm Optimization and Genetic Algorithm) can further mitigate issues of local optimum and parameter sensitivity in traditional models. The research confirms that deep learning models can perform slope stability analysis accurately and effectively, with performance superior to that of traditional methods. Furthermore, this study discusses the current limitations, challenges, and future trends toward “mechanism-data collaborative decision-making,” which not only facilitates the further advancement of slope stability analysis but also provides references for the better integration of deep learning into slope engineering.</p>