Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional maps of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study combines available field data from the Canadian Yukon coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. We developed models using the data for the entire region and separate models for the coastal mainland area and Qikiqtaruk Herschel Islandand. Each model was used to map SOC and N stocks for its respective area. We assessed the performance of the different random forest models by using crossvalidation. We further assessed model results using the Area of Applicability (AOA) method and the quantile regression forest approach, comparing the results and discussing their implications within the context of both methods. We explore local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks. The estimated SOC stock for the upper metre is 48.7 ± 6.6 kg m−2 and the N stock 3.03 ± 0.30 kg m−2. The average SOC stocks vary significantly when creating separate models for subsets of the data. Qikiqtaruk Herschel Island is geologically different from the coastal mainland and has on average lower SOC stocks. Including Qikiqtaruk Herschel Island soil data to predict SOC stocks at the mainland has large impact on the results. Differences in N stocks were not as dependent on the location as SOC stocks and rather differences between individual studies occurred. The results of the separate models show 38.0 ± 5.6 kg C m−2 and 2.87 ± 0.34 kg N m−2 for Qikiqtaruk Herschel Island and 52.5 ± 6.3 kg C m−2 and 3.15 ± 0.32 kg N m−2 for the mainland. These estimates refer to the entire study area, without masking out regions outside the Area of Applicability (AOA). Our results indicate that the spatial perspective, whether regional or local (whole area vs. mainland and Qikiqtaruk Herschel Island separately), can affect the patterns and values represented in the resulting maps, highlighting the importance of scale when interpreting SOC and N stock distributions. Using a more densely sampled, region-specific dataset, our study captures finer regional-scale patterns than previous lower-resolution or pan-Arctic analyses.