Flooding is among the most frequent and costly natural hazards worldwide, with its impacts increasinglyintensified by climate change, land-use change, and expanding human settlements. In the United States, recurrentflood disasters have revealed persistent gaps in predictive capacity, infrastructure preparedness, and the translationof scientific risk assessments into effective disaster risk reduction (DRR) policies. Addressing these challengesrequires analytical frameworks that not only anticipate flood behavior but also support evidence-driven decisionmaking across planning and governance scales. Geospatial Information Systems (GIS) play a critical role in thisprocess by enabling the integration, analysis, and visualization of spatially explicit flood risk data within apredictive modeling environment. This study explores the use of GIS-based predictive flood modeling to supportevidence-driven DRR policy development. At a broad level, the framework integrates hydrological, topographic,climatic, and land-use datasets to simulate flood susceptibility and forecast potential inundation under varyingrainfall and runoff scenarios. Spatial analytics techniques including terrain analysis, hydrological modeling,spatial regression, and scenario-based simulation are applied to identify evolving flood risk patterns and quantifyuncertainty in hazard projections. The framework is demonstrated through case studies from flood-prone regionsin the United States, illustrating how predictive GIS models can inform policy-relevant insights at local, regional,and state levels. The results show how spatially explicit flood forecasts can guide infrastructure investmentprioritization, land-use regulation, and emergency preparedness strategies. In particular, the study highlights thevalue of GIS outputs in aligning predictive flood intelligence with disaster mitigation planning, zoning decisions,and early warning system design. Beyond operational applications, the research emphasizes the role of GISenabled predictive modeling in strengthening DRR policy formulation by improving transparency, supportinginteragency coordination, and enabling adaptive governance under uncertainty. The findings demonstrate thatGIS-based predictive flood modeling provides a robust foundation for transforming flood risk science intoactionable, evidence-driven disaster risk reduction policies across diverse U.S. contexts