Flash floods represent one of the most destructive hazards in arid and semi-arid regions, causing severe damage to infrastructure, livelihoods, and ecosystems. Their assessment is often constrained by limited historical flood records and rapidly changing land-use dynamics. This study develops an integrated and explainable framework for flash flood susceptibility mapping (FSM) in Egypt’s Red Sea Mountains, a coastal zone undergoing rapid urban and tourism expansion. Multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data were processed in Google Earth Engine using Otsu thresholding to generate dynamic flood inventories. These inventories were combined with eleven hydro-topographic and geological predictors within a Multi-Criteria Decision Analysis (MCDA) framework using the Analytic Hierarchy Process (AHP), and further enhanced by three machine learning (ML) classifiers: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM). Model evaluation demonstrated strong predictive skill, with the hybrid AHP–RF model achieving the highest accuracy (AUC = 0.95; overall accuracy = 95%). Shapley Additive exPlanations (SHAP) quantified predictor importance, confirming elevation, runoff volume, and drainage density as dominant drivers of flood susceptibility. Beyond single-hazard mapping, the study introduced an Environmental Sensitivity and Desertification Index (ESDI) and integrated it with FSM to produce a multi-hazard susceptibility map, revealing compound high-risk zones in coastal sabkhas and intensively cultivated floodplains. Scenario-based analyses under RCP 4.5/8.5 and SSP pathways projected significant expansion of high-risk zones under intensified climate forcing and unsustainable socio-economic trajectories. By aligning scenario outputs with the Food and Agriculture Organization) FAO (Standards of Practice to Guide Ecosystem Restoration (2025), the study bridges scientific diagnostics with actionable resilience planning. The integrated framework demonstrates that coupling AHP with ML not only improves predictive accuracy but also enhances interpretability and policy relevance. The outcomes provide critical evidence for disaster risk reduction, land-use management, and ecosystem restoration, offering a transferable model for climate-resilient hazard management in arid coastal environments across Africa and beyond.