Engineering sustainable supply chains in resource-constrained environments has become a critical priority for developing regions facing infrastructural bottlenecks, unpredictable demand patterns, and rising climate-induced disruptions. This study presents an integrated geo-spatial and AI-driven data science framework designed to optimize supply chain resilience, efficiency, and resource allocation across constrained terrains. Using multi-layered datasets that include satellite-derived indicators, road network topology, facility distribution, demographic density, and environmental stressors, the research employs geospatial analytics to map logistical vulnerabilities and influence zones of supply movement. Advanced machine learning and optimization models, including Random Forest, XGBoost, and spatio-temporal LSTM forecasting, are implemented to predict demand flows, identify bottlenecks, evaluate route feasibility, and recommend cost-efficient transport corridors. Spatial interpolation, hotspot detection, and network-based accessibility analysis further support the identification of high-risk operational zones where resource scarcity, weak infrastructure, and climatic variations intensify logistical fragility. The findings demonstrate that integrating remote sensing, GIS-based supply chain mapping, and AI-enabled optimization significantly enhances resource prioritization, reduces transport delays, and improves sustainability outcomes. The study establishes a scalable, data-driven methodology for supply chain planning that is applicable to agriculture, healthcare, disaster relief, and industrial sectors in resource-limited environments.

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