Abstract: Abstract: An accurate prediction of the spatial distribution of Soil Organic Matter (SOM) is of great importance for precision agriculture, farmland quality construction, ecological environment protection, and soil carbon sequestration. However, the accuracy of prediction dominates by the heterogeneity of SOM spatial distribution and its relationship with auxiliary variables. Taking Hailun City, Heilongjiang Province (126°14′-127°45′ E, 48°58′-47°52′ N) of northeast China as the study area, this study aims to accurately and rapidly predict the SOM spatial distribution using a Two-Point Machine Learning Method (TPML) with the climate, topography, socio-economic, and spatial location as the auxiliary variables. The spatial location and auxiliary variables were also integrated to effectively deal with the heterogeneity of SOM spatial distribution and the heterogeneity of its relationship with auxiliary variables. The performance of TPML was then evaluated using the Random Forest (RF), RF regression kriging, inverse distance weighting, and Ordinary Kriging (OK) models. The performances of the models with samples of different sizes were also evaluated using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient between the predict and true value (r), and the coefficient of determination (R2). The results reveal that: 1) The SOM was predicted to range from 1.775 to 7.188 g/kg in the study area, with an average value of 3.179 g/kg. The spatial distribution of SOM spatially varied, with a trend of the high in the east and the low in the west. Meanwhile, the SOM content was positively correlated with the normalized difference vegetation index (NDVI), digital elevation, and mean annual precipitation, whereas, negatively correlated with the gross domestic product, mean annual air temperature, and topographic wetness index, particularly significantly related to the land use, landform, vegetation, and soil type. 2) The TPML presented the highest accuracy of prediction under different sample sizes, with the lowest MAE (0.088-0.097 g/kg) and RMSE (0.116-0.139 g/kg), while the highest r (0.992-0.996) and R2 (0.971-0.985). The MAE and RMSE of the TPML model were improved much more than 0.7 g/kg, while the r and R2 were improved by more than 0.2, and 0.9, respectively, compared with the most frequently-used OK. 3) There is a similar spatial pattern between the standard deviation of prediction errors (theoretical errors) and the actual errors, indicating that the TPML provided reasonable uncertainty estimates for the prediction. Consequently, the TPML can be expected to employ spatial autocorrelation and attribute similarity at the same time for higher spatial prediction accuracy. Anyway, the TPML spatial prediction of variables is feasible for the resource and environment with a certain degree of spatial autocorrelation and available auxiliary data.