Flood early warning in the White Volta Basin of northern Ghana is complicated by unmonitored dam releases from Burkina Faso’s Bagre Reservoir, which existing globally calibrated systems do not account for. We present an end-to-end AI-driven flood early warning system built entirely from open satellite data. An ensemble of Random Forest, XGBoost, and LSTM models trained on GRDC discharge, CHIRPS rainfall, ERA5-Land reanalysis, and a novel JRC-derived Bagre storage proxy achieved Kling-Gupta Efficiency scores of 0.984, 0.974, and 0.957 at 1-, 3-, and 5-day lead times on an independent test period, exceeding the GloFAS v2.1 African median benchmark of approximately 0.35, though direct comparison against GloFAS v4 at Nawuni was not undertaken. A four-tier alert system calibrated to 30-year flood return periods achieved a cross-validated Red-tier probability of detection of 0.902 (false alarm ratio 0.134) at one-day lead, declining to 0.762 at five days; higher-tier skill rests on leave-one-year-out cross-validation rather than held-out evidence, as the test period contains no Orange or Red events. Sentinel-1 SAR mapping confirmed that threshold exceedances correspond to observed inundation extents of 50 to 149 km². The system integrates into Ghana's existing myDEWETRA-VOLTALARM platform without requiring new institutional infrastructure.