[Objective] This study develops an automated and reproducible framework for dynamic urban flood risk assessment by integrating multi-source satellite imagery with machine learning algorithms. It aims to: (1) establish a high-precision flood extraction methodology utilizing Google Earth Engine (GEE); (2) evaluate the performance of an optimized XGBoost-RF ensemble model against traditional machine learning algorithms; and (3) quantify the primary driving mechanisms of urban flooding. Innovation Statement:The primary innovation of this research lies in the synergistic integration of Sentinel-1 (SAR) and Sentinel-2 (Optical) data to overcome cloud interference and urban spectral noise. This is coupled with a robust three-step factor screening protocol and a weighted XGBoost-RF ensemble architecture specifically designed to address the non-linear complexities and class imbalance inherent in urban hydrological systems. [Methods] The extreme flood event of June 2024 in Changsha, China was taken as a case study. The GEE cloud platform was utilized to develop a collaborative water extraction model. Sentinel-1 VV-polarization SAR imagery (Lee-filtered) was fused with Sentinel-2 MNDWI-processed optical data. An OTSU adaptive thresholding algorithm was applied for initial water body extraction, followed by SRTM DEM-based slope filtering (<5°) to eliminate terrain shadows and connected component analysis for noise reduction. For risk modeling, 14 initial hazard factors were selected across four dimensions: topography, human activity, natural conditions, and historical disaster records. To ensure model robustness, a three-step screening protocol was implemented: (1) Pearson correlation analysis (∣r∣<0.7); (2) multicollinearity diagnosis (VIF<10); and (3) ReliefF algorithm for variable importance quantification. Four models were constructed and compared: MLP (Adam-optimized), LightGBM (with SMOTE oversampling), XGBoost, and a weighted XGBoost-RF ensemble (0.6∶0.4 ratio). Model training was conducted on a 2017 historical dataset (600 samples), with validation performed against an independent 2024 dataset (400 samples) using AUC, accuracy, and precision metrics. [Results] The GEE-based collaborative model effectively mitigated the spectral limitations of single-sensor data, enabling the high-precision, automated mapping of flood inundation during the June 2024 event. This provided a reliable empirical sample set for the risk assessment framework. Factor importance analysis identified NDVI and surface relief as the most significant drivers of flood occurrence in Changsha (Variable Importance >0.036). Comparative evaluation of the four algorithms revealed that theXGBoost-RF ensemble modelachieved the highest predictive accuracy (79.19%) and AUC (0.849 7), outperforming LightGBM, optimized MLP, and standalone XGBoost. The model demonstrated high reliability, with a precision of 83.02% and a specificity of 85.00%. Spatial analysis further revealed that flood risk is primarily clustered in gentle slope zones (0-10°), areas adjacent to river networks, and regions with high-intensity human activity. These predicted high-risk zones showed strong alignment with the actual inundation extent, confirming the model’s ability to capture the spatial heterogeneity of urban floods. [Conclusions] This research validates a synergistic framework for dynamic urban flood risk mapping, yielding three primary insights for disaster management: (1) The integration of Sentinel-1 and Sentinel-2 data on the GEE platform provides a robust, automated solution for rapid flood monitoring. By overcoming “cloud contamination” and urban spectral noise, this approach ensures that “ground truth” data remains accessible even under extreme weather conditions.(2) The XGBoost-RF ensemble architecture represents a significant advancement in risk prediction. By balancing the strengths of boosting and bagging techniques, the model effectively addresses the non-linear complexities and class imbalances of urban hydrological systems, offering superior stability and generalization compared to single-algorithm models.(3) The identification of NDVI and surface relief as dominant drivers challenges conventional risk assumptions. The high vulnerability of high-vegetation areas during extreme rainfall suggests that “saturation-excess” runoff is a critical factor in urban flood evolution.