Under the combined influence of the frequent occurrence of extreme precipitation due to the intensification of global climate change and the wild expansion of cities, the risk of urban waterlogging is constantly increasing, posing a huge challenge to urban governance. Therefore, it is necessary to study effective strategies to identify and address the potential risk points in advance to mitigate the risk of urban waterlogging. Considering that a single data source is difficult to accurately depict the surface and underground coordinated waterlogging mechanism, a method for urban waterlogging identification and water level simulation based on multi-source heterogeneous fusion data is proposed. By integrating the high-precision digital elevation model (DEM, 0.05 m) generated from 3D laser point clouds with the 3D urban pipeline network data, and combined with the rainstorm intensity model, the micro-depressions of roads are accurately extracted through GIS spatial analysis. The drainage status is determined based on its spatial relationship with the rainwater grate service area. Depressions not covered by any grates are identified as high-risk areas. Taking a certain street in Nansha District, Guangzhou City as the research object, the comparison between computational simulation and actual data indicates that the method can efficiently locate the risk points of water accumulation, which provides a data-driven and efficient risk assessment tool for municipal departments, and is of great practical significance for urban disaster reduction governance and future planning.