Hyperspectral remote sensing combined with machine learning represents an important means of soil chemical element monitoring. However, there is a problem regarding the lack of universality in element content prediction models across different soil types. We propose an improved standard-sample calibration transfer method [the direct standardization (DS), piecewise direct standardization (PDS), and spectral space transformation (SST) calibration transfer methods combined with the standard sample difference pairing (SSDP) method], which is derived from the standard-sample calibration transfer method commonly used in spectrometer calibration, to study the transferability of machine learning prediction models between different soil types. To study the effectiveness of the improved standard-sample calibration transfer method, albic black soil and typical black soil from northeast China are selected as the research objects. We established partial least squares (PLS) and backpropagation neural network (BPNN) prediction models for the soil organic matter (SOM) content of albic black soil. Subsequently, the albic black soil model is used to predict the SOM content of typical black soil. The results show that the improved standard-sample calibration transfer method significantly improves the prediction ability of the albic black soil model for typical black soil, with the determination coefficient (R2), root mean square error (RMSE), and relative percent deviation (RPD) of the typical black soil validation set increasing to 0.616 to 0.774, 2.184 to 2.568, and 1.500 to 1.763, respectively. Among the methods utilized, SSDP-PDS, SSDP-DS, and SSDP-SST exhibit the best, second-best, and worst performances, respectively. In addition, the methodological framework is also effectively applicable to the prediction of SOM content in albic black soil using a typical black soil model. Finally, compared with the sample mixing method, the improved standard-sample calibration transfer method demonstrates higher prediction accuracy. This study holds significance for research on the universality of element content prediction models across different soil types.