Flood and ground-deformation monitoring will be integrated into a single decision-support platform through this project’s hybrid fusion approach. The work combines millimeter-scale interferometric synthetic aperture radar (InSAR) deformation maps, uncrewed aerial vehicle light detection and ranging (UAV-LADAR/LiDAR) terrain and drainage models, and synthetic aperture radar (SAR) soil-moisture indices to generate a unified flood-deformation risk index. A physics-guided machine learning model trained on historical flood and subsidence records will automate hazard detection with a target accuracy of at least 90%. Operational use is built into the design. An edge-computing prototype packaged in a Docker container will process new sensor inputs within 24 hours and issue actionable alerts through a secure web dashboard. Deployment for stakeholders includes user documentation, source code delivery, and training workshops to support adoption. A commercialization brief will also define market potential, intellectual property strategy, and licensing pathways to accelerate transfer to additional corridors.