Based on high-precision hydrodynamic model and machine learning technology, a high-resolution rapid forecasting model of mountain flood disaster was constructed by using extremely randomized trees and KNN algorithm. The overall reliability of the model was evaluated by three indexes: determination coefficient, mean absolute error and root mean square error. At the same time, the discharge of the outlet section of the basin was intercepted to verify the prediction performance of the model. The results show that the prediction results of the model are basically consistent with the inundation range of the hydrodynamic model simulation results, the average relative error of the basin inundation range is less than 5%, and the model is stable and reliable as a whole. The relative error of discharge at the outlet section of the basin is less than 10%, the average relative error of water depth and velocity at the section is less than 5%, and the prediction performance of the model is good. The model can calculate the maximum inundation situation and output the inundation range map within 10 s, which can provide sufficient lead time for emergency decision-making and assist decision-makers to take better countermeasures.