Compared with large-scale geological disasters such as landslides and earthquakes, small-scale urban geological disasters such as collapses and ground fissures are often overlooked. However, the socioeconomic impacts of these small-scale events can often exceed those of larger disasters in major cities. Although the use of machine learning for susceptibility assessment is a well-established aspect of large-scale geological disaster prevention, insufficient disaster samples and resultant dataset imbalances have hindered its application to small-scale urban geological disasters. To address this issue, we propose a comprehensive process that involves defining disaster risk areas to expand disaster sample points, optimizing the extraction method for training and test sets to balance the dataset, and selecting models with high generalization capabilities to enhance prediction accuracy. By focusing on all urban road collapse incidents from 2015 to 2023 in Binjiang District, Hangzhou’s most economically developed areas, we demonstrated the reliability of this process. Furthermore, to support urban policymakers, we employed the SHAP model to demystify the predictive process and assess the impact of factors, providing reliable analytical results. Our approach provides a replicable and comprehensive solution for susceptibility assessments of cities impacted by small-scale geological disasters using machine learning and subsequent analyses.