Surface soil moisture (SSM) is a critical indicator of crop growth conditions, and its accurate retrieval is essential for agricultural monitoring. Integrating multispectral and microwave remote sensing data can enhance SSM estimation, but discrepancies among platforms often reduce accuracy at local scales. In this study, we fused Sentinel-2 and UAV multispectral images through resampling to generate fusion data, which were then combined with miniature synthetic aperture radar (MiniSAR) data. A modified water cloud model (WCM) was applied to mitigate vegetation effects on radar backscattering coefficients. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and extreme learning machine (ELM)—were employed to retrieve SSM. Field measurements at two depths (0–10 cm and 0–20 cm) over winter wheat fields in Xunxian, Hebi City, Henan Province, China, were used for validation. Results showed the following: (1) Fused multispectral data improved retrieval accuracy compared with single-satellite data, with the best configuration (fused data + VV + RF) achieving an R² of 0.85 and an RMSE of 1.51% at 0–10 cm. (2) At 0–20 cm, the fused data combined with VV polarization and XGBoost achieved the best performance (R² = 0.67, RMSE = 2.61%). (3) ELM exhibited the largest accuracy improvement after incorporating fused data, with R² increases up to 0.40 and RMSE reductions up to 18.24%. These results demonstrate the strong potential of multi-platform multispectral fusion combined with MiniSAR data for improving field-scale SSM retrieval in winter wheat regions.