Abstract In small alpine watersheds, abrupt rainfall‐ or meltwater‐driven runoff leads to dramatic variations in flow width over uneven channel beds, thereby complicating discharge monitoring. Using a typical watershed near the Yarlung Tsangpo Grand Canyon as the study area, we presented a workflow combining imagery and seismic data to estimate the instantaneous discharge with machine learning. Results show that image‐based flow width correction effectively improved discharge records to provide a better basis for modeling. For hourly instantaneous discharge estimation, the machine learning model achieved good performance, with mean coefficients of determination of 0.77 for the training, 0.68 for the validation, and 0.66 for the independent testing, and corresponding mean Kling–Gupta efficiency values of 0.83, 0.79, and 0.75, respectively. Using minute‐level seismic data, we further detected 179 suspected flood events between July and October 2025 and analyzed their hydrological characteristics. In general, this proposed framework could address the limitations of traditional monitoring approaches and support water resources management in similar small watersheds.