Emergency medical services (EMS) play a critical role in urban healthcare systems, where population density and traffic congestion often lead to increased response times. This project aims to enhance EMS efficiency through geospatial analysis using the H3 hexagonal grid system, enabling strategic deployment and redeployment of EMS vehicles to ensure optimal coverage and minimize response delays. Implementation results demonstrate an 8.0% reduction in average EMS response time and a 41.6% decrease in maximum ambulance travel distance compared to random allocation. These improvements have significant implications for urban healthcare delivery, potentially reducing mortality rates by enabling faster emergency responses. However, challenges remain in real-world deployment, including data accuracy, infrastructure integration, and scalability across diverse urban environments. The methodology leverages H3-based spatial partitioning to divide the operational area into structured zones, facilitating precise resource allocation. Severity scores are computed using an inverse ranking system, normalizing incident data to prioritize high-risk zones. A dynamic event-driven model continuously updates deployment strategies based on real-time incidents, optimizing ambulance placement and availability. This ensures that EMS vehicles are positioned in close proximity to potential emergencies, improving access to critical care. By implementing this integrated system, the project seeks to reduce EMS response times, improve ambulance utilization, and enhance overall patient outcomes. The research findings demonstrate that real-time geospatial analysis and adaptive redeployment strategies significantly enhance emergency response efficiency. Future enhancements will focus on refining predictive models for even more effective resource management.