Municipalities worldwide struggle with slope instability, a particularly pressing issue in cities such as Salvador, Brazil, where rugged terrain, escarpments, and complex geology create a high risk of instability. The complexity of the problem is evident in the variability of terrain properties, the bedrock’s inherited structural features, and anthropogenic action. This range of variables makes it well-suited to machine learning (ML) approaches for instability prediction. Although ML has experienced an impressive recent boost, only a few cases have applied ML to real-world instability events. In this paper, a data bank of hydromechanical properties of soils is used in conjunction with a digital terrain model (DTM) and different geo-referenced information, including rainfall, vegetation coverage, geological structures, sewage collection/treatment status, and residential density, to predict the occurrence of soil mass movements and related emergency calls to the municipality from the population living in risk areas. 13,522 emergency calls were considered during the period from 2020 to 2025. Excellent predictive performance, with an R² ≈ 0.98 consistently across both the validation and testing phases, was obtained in this original study. This strong result underscores the viability of machine learning as a powerful tool for this kind of problem, particularly within municipal warning systems.