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Seabed sediment maps underpin a variety of marine research endeavours. Seabed mapping data are available for many regions, but these usually provide discrete classifications which obscure underlying continuous properties of the sediments. Other areas are poorly surveyed, e.g., polar regions which are inaccessible due to ice cover. Here, we focus on the inaccessible North East Greenland shelf for which there are almost no seabed sediment data. We trained a random forest model to predict sediment classes from an existing map of the well‐surveyed neighbouring Barents Sea, using data on bathymetry, currents and waves. We then used our model to predict the unknown sediment distributions off East Greenland. In the process, we generated some new spatial data on previously un‐mapped properties of the Barents Sea, such as mean grain size, organic carbon and nitrogen content, porosity and …