Development of advanced flood-risk monitoring tools is the focus of this funded project. Work will produce a validated hybrid fusion algorithm that combines Interferometric Synthetic Aperture Radar (InSAR) deformation maps, Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) terrain and drainage models, and Synthetic Aperture Radar (SAR)-derived soil-moisture indices into a unified flood-deformation risk index. A physics-guided machine learning model, trained on historical flood and subsidence records, will automate hazard detection with at least 90 percent accuracy. Deployment includes an edge-computing prototype packaged in a Docker container to process new sensor inputs within 24 hours and issue alerts through a secure web dashboard. Project deliverables also include user documentation, source code, training workshops, and a commercialization brief outlining market potential, intellectual property (IP) strategy, and licensing pathways for broader deployment.