The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short due to a limited understanding of UFs’ nature, frequently relying on disaster factors analogous to those used for natural floods while neglecting key urban characteristics, limiting the accuracy of UFS estimates. To address these challenges, we propose a novel framework for UFS assessment. Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. Guangzhou was selected as the study area, where machine learning methods were employed to calculate UFS, and Shapley Additive Explanation was utilized to quantify the contributions of employed factors. We evaluated the significance of urban factors from three perspectives: classifier performance, map accuracy, and factor importance. The results indicate that (1) urban factors hold significantly greater importance compared to other factors, and (2) the incorporation of urban factors markedly enhances both the performance of the trained classifier and the accuracy of the UFS map. These findings underscore the value of integrating urban factors into UFS assessments, thereby contributing to more precise UF management and supporting sustainable urban development.