The rapid adoption of electric vehicles (EVs) necessitates the development of an optimized and accessible charging infrastructure to support sustainable urban mobility. Existing approaches to EV charging station siting often suffer from limitations such as static modelling, lack of spatial intelligence, and insufficient integration of multi-criteria decision making. This paper presents a comprehensive GIS-based site selection framework that leverages geospatial data and multi-criteria analysis to identify optimal locations for EV charging infrastructure. Unlike traditional methods, the proposed model incorporates real world factors including land use, traffic flow, power grid proximity, and environmental constraints, enabling spatially intelligent and scalable planning. The framework addresses key drawbacks in recent literature by providing a dynamic, stakeholder-responsive, and regionally adaptable tool for strategic infrastructure deployment. Results demonstrate that integrating geographic information systems with decision-support techniques significantly enhances the accuracy, equity, and sustainability of EV charging network expansion. This study contributes a replicable planning solution that supports data-driven policymaking and accelerates the transition to low-carbon transportation ecosystems.