With increasing urban pluvial flood risks, proposing a real-time simulation method is essential. However, accurate simulation of spatiotemporal flood evolution is often impeded by incomplete or missing drainage data. This study proposes a hybrid method where a machine learning module is applied to generate point waterlogging depth for immediate calibration of equivalent infiltration and flood maps in the equivalent drainage module to address this issue. The accuracy and efficiency of hybrid method in flood real-time simulation under missing drainage data are highlighted by comparing with two hydrodynamic models. The outcomes evince that the waterlogging simulation deviation of the hybrid method is less than 0.1 m during design storms, while the computational efficiency can ideally reach up to 5 times of the traditional 1D/2D coupled hydrodynamic model. Overall, the hybrid method offers a promising solution for early warning and mitigation of urban pluvial floods, especially for cities lacking drainage data.