This project proposes a method to analyze the traffic and urban mobility of a locality with advanced visualization and understanding mechanisms. urban areas worldwide face escalating challenges in managing transportation systems efficiently. This research delves into the realm of urban mobility analysis, leveraging geospatial data to extract insightful patterns and trends. The project aims to contribute a comprehensive understanding of the dynamics governing urban transportation, enabling informed decision- making for city planning and infrastructure development. The methodology encompasses the collection of extensive geospatial datasets, including traffic flow, public transportation routes, and demographic information. Utilizing advanced geospatial analysis techniques, such as Geographic Information Systems(GIS) and spatial analytics, the study explores spatial-temporal patterns of traffic congestion, public transit usage, and pedestrian movement. Machine learning algorithms are employed to predict future mobility trends and assess the impact of potential interventions. The findings of this research offers valuable insights for urban planners, policymakers, and transportation authorities. By identifying key bottlenecks, optimizing transit routes, and understanding the interplay between land use and growing populations and foster sustainable urban development. This project not only contributes to academic understanding of urban mobility but also provides actionable recommendations for creating smarter and resilient cities. The integration of geospatial analysis techniques in urban planning emerges as a powerful tool to address the complexities of modern urban transportation systems. Keywords: informed decision-making, geospatial data, Geographic Information System(GIS), spatial analysis, sustainable urbandevelopment.