Lakes are ubiquitous with high-latitude ecosystems, covering up to 60 percent of the land surface in some regions. Due to their influence on an array of key biogeophysical processes, the recent decline in lake area (via gradual and abrupt) observed across permafrost ecosystems may hold significant implications for shifting carbon and energy dynamics. Since lakes are often highly dynamic, understanding the main drivers of lake area change may ultimately enable the prediction of lake persistence in a warmer climate; key to anticipating future carbon-climate feedbacks from Arctic ecosystems. Here we conducted a data-driven analysis of >600k lakes across four continental-scale transects (Alaska, E Canada, W Siberia, E Siberia), combining remote sensing-derived lake shape parameters and spatial dynamics with other ecosystem datasets, such as ground temperatures, climate, elevation/geomorphology, and permafrost landscape parameters. We grouped our lake-change dataset into non-drained, partially and completely drained lakes (25-75 %, >75% loss) and used the RandomForest Feature Importance to calculate the relative importance of each parameter. Furthermore we predicted the probability of lake drainage under current environmental conditions and changing permafrost temperatures. Initial results suggest a strong importance of ground temperatures, lake shape, and local geomorphology on lake drainage. Spatially coarser datasets of permafrost and thermokarst properties did not reveal correlations with the result. Our drainage prediction results show distinct spatial patterns, which are matching regional lake drainage patterns. Our model estimated ground temperature as one of the main impact factors, with an increased drainage likelihood in permafrost regions from -5 to 0 °C. Going forward, we will further test for short term influences, such as extreme weather events and wildfire on widespread lake drainage. As this analysis is purely data-driven, a comparison or combination with physics-based models and predictions will help to better validate our analysis.