The study offers a geographically conscious supply chain network optimization model that is developmentally oriented and is based on the geospatial diversity of the ten key regions of India. The conventional supply chain models fail to consider the geographical differences in traffic, infrastructure, and inventory availability, which limits the economic performance and developmental equity. This study determines the areas that require the most optimization and investment by combining variables of spatial data that comprise shipping cost, traffic congestion, inventory levels, and disruption risk into a composite Cost-Time-Risk index. The study uses geospatial mapping and multi-objective modelling applications to review logistical inefficiency and model interventions that are suitable for the regional requirements of logistics. The results indicate a strong contrast between congested high-risk areas and other regions with great potential in terms of optimization. Besides the identification of bottlenecks that are critical, the analysis also offers practical information regarding the routing strategy, stocking policy, and capacity planning. The research highlights the tactical imperative to abandon centralized, one-size-fits-all logistics models and instead introduce regionally adaptive, resiliency-based models. Going ahead, the research proposes the inclusion of artificial intelligence-based tools and real-time data analytics so as to have dynamic and self-optimizing supply chains. Lastly, it demands coordinated regional planning, which places infrastructure development, policy formulation, and technological advancement on the same platform, so that spatial optimization turns out to be a driver of inclusive growth and long-term supply chain resilience.