Slope stability analysis is a critical component of geotechnical engineering, for the assessment of rainfall-induced failures that pose significant risks to infrastructure and human safety. Traditional methodologies, including the Limit Equilibrium and Finite Element Methods, encounter challenges with accurately representing the complex relationships among precipitation, soil characteristics, and environmental variables. Machine learning (ML) methodologies are growing as effective methods for enhancing predictive accuracy through the integration of various geotechnical and climatic parameters. This literature review contains a thorough examination of machine learning applications in the assessment of slope stability, emphasizing recent advancements, existing challenges, and prospective research opportunities aimed at improving landslide prediction, risk mitigation, and decision-making.