Reliable and rapid flood damage estimation is crucial for both disaster risk reduction and crisis management. Yet, existing models primarily focus on riverine floods, neglecting urban pluvial floods – a substantial gap, as heavy rainfall can lead to flooding virtually anywhere. Here, we present FlooDEsT, a new machine learning (ML)-based tool to rapidly estimate building-level damage from urban pluvial flooding with four key improvements, compared to traditional models. First, the model was trained specifically on damage data from urban pluvial flood events. Second, the tool utilises XGBoost, a ML technique capable of capturing complex non-linear data relationships. Third, the tool efficiently utilises geographical information only as necessary, reducing pre-processing time. Fourth, to address the common challenge of missing data, the tool uses smart random sampling techniques to impute building-level features that are representative to buildings affected by this flood pathway, reducing exposure bias. The tool’s computational performance was evaluated in two German case studies, involving about 2300 and 440,000 buildings. The tool provided damage estimates in respectively 2.1 to 5.6 seconds per thousand buildings, representing a 2.7- to 6.6-fold improvement in speed over a baseline approach. FlooDEsT tackles critical gaps in damage modelling, offering valuable support for disaster preparedness and response.