Urbanization and rising vehicle ownership have intensified the challenge of finding parking in metropolitan areas. This research presents the Dynamic Parking Space Allocation System (DPSAS), an advanced framework for real-time parking management optimization. The system models the urban environment as a weighted graph, where intersections and parking zones are nodes, and roads, affected by dynamic traffic conditions, form the edges. A key innovation is the use of hexagonal grid partitioning, which efficiently segments the urban space, reducing the search area and accelerating computational processes for real-time applications. Additionally, DPSAS features dynamic adaptation mechanisms that continuously recalibrate routes and parking availability in response to evolving traffic conditions. The system adapts to dynamic factors such as travel time, cost, and parking availability, ensuring user-centric optimization and adaptability in fluctuating urban settings. Extensive simulations demonstrate that DPSAS significantly reduces parking search times, optimizes traffic flow, and improves resource utilization. The results highlight the system’s robustness and scalability, positioning DPSAS as a transformative solution to urban parking challenges, bridging gaps in existing systems, and enhancing urban mobility.