Iran’s electricity system faces concurrent challenges of power deficits, severe water scarcity, and environmental degradation, necessitating planning approaches that integrate resource constraints with long-term sustainability objectives. This study develops a national-scale decision framework that combines satellite-based environmental data, geospatial analytics, and mixed-integer optimization to identify optimal power plant siting across 31 provinces of Iran. Renewable resource indicators (solar radiation and wind speed) and air quality constraints (NO2) were derived from remote sensing platforms and integrated with electricity deficits, population pressure, and land acquisition costs within a unified optimization model implemented in GAMS. The results demonstrate that a renewable-dominant configuration minimizes total system cost while substantially reducing environmental stress. The optimized portfolio supplies 7,430 MWh, of which 5,650 MWh originates from solar, 180 MWh from wind, and only 1,600 MWh from thermal generation. Compared with fossil-based expansion scenarios, the proposed allocation reduces annual water consumption by over 102 billion liters and limits new emission-intensive capacity in highly polluted demand centers. Spatial results reveal a strategic decoupling between electricity demand hubs and generation sites, relying on interregional transmission to enhance supply reliability without intensifying local environmental pressures. By explicitly incorporating water scarcity as a binding planning constraint, this study advances national-scale power system modeling beyond demand-driven expansion and provides a transferable framework for climate-informed energy planning in water-stressed regions.