Lakes dammed by surging glaciers can fill and drain within one season, often under cloud. We present IceWatch, which combines a convolutional classifier of Sentinel-2 scenes (RiskFlow), forecasts of glacier velocity (TerraFlow) and land-surface temperature (TempFlow), and a probabilistic fusion stage (FusionFlow). We test it at Shisper Glacier, Karakoram, where an ice-dammed lake drained four times between 2019 and 2022, with all data taken over the lake and its dam, and scenes labelled from a published chronology. Trained on earlier years and tested on 2022 to 2025, RiskFlow detected both post-outburst scenes of 2022 with 2 false alarms in 102 other scenes. With each year held out in turn, it detected 4 of 7 post-outburst scenes with no false alarm in 151 others, whereas a water-index rule detected all 7 but raised 26 false alarms. A model trained on 103 other ice-dammed lakes did not transfer. TerraFlow was 30 % more accurate than persistence, and TempFlow beat persistence and climatology two days ahead. Three of the four outbursts followed cooler-than-normal weeks, and neither an agreement rule nor the fusion improved on RiskFlow. IceWatch can confirm drainage from the next clear scene; anticipating outbursts will require observations of lake filling.