Abstract: Soil is closely related to its formative environment. How to use soil properties to accurately predict the associated environmental information is an important research problem in soil forensics. About 746 soil samples were selected from Beijing, Tianjin, Hebei, Shandong, Anhui and Jiangsu in eastern China. Four key environmental information (elevation, average annual temperature, average annual rainfall and surface temperature) were predicted based on basic soil properties and spectral data using two machine learning models (neural network and random forest). Root mean square error (RMSE), determination coefficients (R²) and concordance correlation coefficient (CCC) were used to calculate the prediction accuracy. Results showed that the prediction accuracy of the two methods were between 0.39 and 0.61. Compared with the neural network model, the spatial variation of environmental variables using random forest model were increased by 9.9% (elevation), 16.5% (average annual temperature), 10.3% (average annual rainfall), and 10.9% (surface temperature). And altitude and rainfall in this study area showed a better prediction accuracy than the other environmental variables. This suggests that the machine learning methods can be effective for predicting environmental information based on soil properties. This study provided a technical support for identifying the source of unknown soil samples in soil forensics.