AbstractAlpine peatlands are one of the carbon reservoirs, provide vital ecosystem services, and support endangered biodiversity. However, they are globally understudied, including those in the Italian Alps, which host thousands of small sites averaging under 1 ha. Their complex geomorphology makes detection challenging with single‐sensor, low‐resolution remote sensing imagery. In the last decade, high resolution multi‐source imagery (e.g., Sentinel series) and the cloud‐based computation platforms (e.g., Google Earth Engine—GEE) have become available. Using these advancements, we developed a method to map the distribution of alpine peatlands. Utilizing 1 and 30 m digital elevation models (DEMs), optical, and microwave data sets, our method exploits a pixel‐based Random Forest (RF) machine‐learning algorithm on the GEE platform to map alpine peatlands in the Avisio River basin of the Italian Alps. The results show that the data set of single‐year time series multi‐source imagery, binary samples (peatland or non‐peatland), and 30 m DEM is the most effective for mapping alpine peatlands. The method achieved an overall accuracy of 90.5%, with 81.8% true positives and 0.8% false positives. The method identified 11.635 km2 of alpine peatlands, surpassing the 7.764 km2 documented in official inventories, this discrepancy may be due to overestimation but also gaps in the existing reference inventory. In the classification process, DEM derived variables proved more effective than optical and microwave derived variables. Variable importance analysis in the RF model indicated that elevation is the most influential factor, while the microwave derived VV‐VH difference (ascending track) contributes the least.