Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation-machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0-100 cm) for South Africa's Vhembe District (2017-2022). Five algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)-were calibrated using similar to 50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R-2 = 0.96-0.97, RMSE < 0.025 cm(3)/cm(3)), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm(3)/cm(3), field capacity = 0.35 cm(3)/cm(3)) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84-0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79-94% of maize areas required irrigation during dry years (2017-2019, 2021-2022) versus 32% in wet 2020-2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation.