A Spatial Decision Support System (SDSS) is an advanced computer-based tool designed to assist in making decisions that involve spatial or geographic data. It integrates geographic information systems (GIS), analytical models, and decision-making frameworks to support complex problem-solving in areas where spatial relationships are critical. In essence, an SDSS combines spatial data analysis, visualization, and scenario evaluation to provide decision-makers with comprehensive insights, enabling informed choices that account for both environmental and social factors. The system is particularly valuable in environmental health and management because it allows for the assessment of potential health impacts of environmental changes, pollution sources, and resource management strategies. The core components of an SDSS include a spatial database, modeling tools, user interface, and decision-support modules. The spatial database stores geographic data such as land use patterns, population density, water bodies, and industrial locations, alongside environmental health data like disease prevalence, pollutant concentrations, and exposure zones. This database allows users to retrieve, update, and query data in ways that reflect real-world spatial relationships, providing the foundation for accurate analyses. The modeling tools integrated within SDSS enable simulation of environmental processes, risk assessments, and predictive analyses. For example, these tools can model the dispersion of air pollutants from an industrial facility and predict the potential health outcomes for surrounding communities, facilitating proactive interventions. The decision-support modules of SDSS are designed to translate complex spatial data and model outputs into actionable information. This includes tools for multicriteria decision analysis (MCDA), which help evaluate different options based on multiple criteria such as environmental sustainability, public health impact, and economic feasibility. In environmental health contexts, MCDA can prioritize areas for intervention, identify high-risk populations, and recommend mitigation strategies for hazards such as contaminated water, vector-borne diseases, or industrial emissions. The user interface is critical for enabling decision-makers, who may not be GIS experts, to interact with data visually through maps, charts, and scenario comparisons. Intuitive visualizations of spatial relationships and predicted outcomes allow for more effective communication between technical experts and policy-makers. One of the main applications of SDSS in environmental health is in urban planning and public health management. By integrating health surveillance data with spatial information on infrastructure, land use, and environmental hazards, SDSS can help identify neighborhoods at higher risk for diseases linked to environmental factors, such as respiratory illnesses