Soil moisture (SM) is a crucial meteorological parameter affecting agricultural production and ecosystems, so accurately capturing SM is significant for farm irrigation and ecosystem protection. The delay Doppler maps data of the Cyclone Global Navigation Satellite System (CYGNSS) contains characteristic information related to SM, providing a new observation method for SM observation. First, the CYGNSS data are matched to the soil moisture active and passive dataset of 36 km×36 km grid by the nearest neighbor method. Then, a random forest regression (RFR) model is constructed for training and prediction. It is found that the RFR model based on the combination of 13 characteristic variables can accurately predict SM. On the training set, the correlation coefficient (R) of the model is 0.966, the root mean square error (RMSE) is 0.026 cm3 cm−3, and the mean error (ME) is 0.000 cm3 cm−3. On the test set, the R of the model is 0.903, the RMSE is 0.041 cm3 cm−3, and the ME is −0.001 cm3 cm−3. Simultaneously, to study the advantages of RFR prediction, this study also compared it with multiple linear regression (MLR) predictions. The results show that the RFR algorithm has higher accuracy both in the training set and the test set. Compared with MLR, R increased by 26.3% in the training set and RMSE decreased by 59.4%. R increased by 17.1% in the test set, and RMSE decreased by 35.9%. This demonstrates that the RFR model with multi-characteristic variables has good reliability, making it an efficient way to use satellite remote sensing data for SM retrieval.