Redeveloping high-priority industrial sites requires a robust understanding of environmental risks to ensure safe land reuse, regulatory compliance, and sustainable urban planning. This study presents a GIS-Enhanced Environmental Risk Assessment Model designed to integrate spatial analytics, environmental datasets, and contaminant-specific indicators to improve the evaluation of legacy pollution and potential human–ecosystem exposure pathways. The model synthesizes multi-layer geospatial information, including soil chemistry, hydrogeology, groundwater flow patterns, historical land-use records, atmospheric dispersion parameters, and proximity to sensitive receptors such as residential zones, schools, wetlands, and surface water bodies. Through the use of spatial interpolation, contamination hotspot mapping, and weighted overlay analysis, the model generates a comprehensive risk index that supports evidence-based prioritization of remediation interventions across industrial redevelopment sites. The proposed framework incorporates environmental thresholds, regulatory standards, and EPA-recommended screening levels, enabling planners and regulatory agencies to identify areas that exceed acceptable contaminant limits. GIS-based spatial modeling further helps visualize pollutant migration pathways, delineate zones of elevated exposure potential, and simulate alternative redevelopment scenarios under varying remediation strategies. The integration of remote sensing datasets enhances temporal monitoring of site conditions, enabling early detection of land-surface changes, vegetation stress patterns, and hydrological alterations that may signal evolving contamination risks. A decision-support module embedded in the model provides a transparent methodology for ranking redevelopment sites based on environmental severity, socio-economic implications, and projected remediation costs. Application of the model to representative industrial brownfield sites demonstrates its ability to improve assessment accuracy, reduce uncertainties associated with heterogeneous contamination, and enhance stakeholder engagement through intuitive spatial visualizations. The results underscore the critical role of GIS-driven approaches in modern environmental management and sustainable land-use planning. By offering a scalable, data-driven assessment tool, the study contributes to improved environmental stewardship and promotes safer redevelopment of industrial zones affected by legacy pollution. The GIS-Enhanced Environmental Risk Assessment Model ultimately strengthens regulatory decision-making, supports community health protection, and advances the transition toward resilient, sustainable, and economically productive post-industrial landscapes.