Urban flooding recurs in the same low-lying districts of coastal megacities every rainy season, and Lagos, Nigeria, is no exception. Existing flood risk maps for the city are fragmented across single-hazard studies, rely on expert-assigned weights never checked against observed flooding, and offer no explanation for why one area is classified as riskier than another. This study developed and validated a three-layer geospatial disaster intelligence framework, GeoAID, for Amuwo Odofin Local Government Area. Fourteen satellite-derived conditioning factors and a Sentinel-1 SAR-derived flood inventory were assembled via Google Earth Engine. Logistic Regression, Random Forest, and XGBoost were trained and compared; Random Forest was selected, achieving 0.798 ROC-AUC under random train-test splitting, falling to 0.642 under spatial five-fold cross-validation, a gap reported rather than concealed. SHAP identified vegetation cover (NDVI) as the dominant predictor, converging with an independent city-scale land-cover study using unrelated data and methods. The resulting map showed 229,402 residents (61.1% of the LGA's population), 16 of 35 schools, and 55 of 102 health facilities within High or Very High risk zones. All outputs were deployed in a public dashboard linking structural risk to near-real-time rainfall. The framework demonstrates that explainable machine learning on sparse, SAR-derived training labels can produce validated, auditable flood risk information in a data-scarce urban setting, without requiring ground-truth flood records.