In the present study, digital images of black soil were identified by their red (R), green (G), and blue (B) color components that correlate with SOM content, and then used to construct predictive stepwise multiple regression models (SMRM) and neural network methodologies (NNM) for SOM content. Our findings revealed that the absolute value of correlation coefficients (|r|) between each original color component and SOM content followed the order: R>G>B, with |r| of 0.67, 0.65 and 0.50, respectively. The |r| value increased after logarithmic and square root transformations, but decreased following reciprocal and square changes. The determination coefficient (R²) for SMRM training and validation sets with and without transformations fall within the range of 0.43 to 0.50 and 0.46 to 0.50, and the root mean square error (RMSE) ranged 1.28%–1.39%, and 1.31%–1.39%, respectively (P<0.001). Specifically, SMRM incorporating logarithmic and square root transformations of R, G and B color components demonstrated superior predictive performance and higher accuracy. Subsequently, multi-layer perceptron neural networks using original values of R, G and B color components successfully estimated SOM content, with R² of 0.49 and 0.49, and RMSE of 1.31% and 1.28% for the training and validation sets, respectively (P<0.001). Therefore, both SMRM and NNM provided effective estimates in SOM content for black soil using its digital image. Our findings provide an operational prediction model for the rapid assessment of SOM content of black soil in northeast China.