India leads the world in traffic accidents and fatalities, with a disproportionate 11% of global accidents occurring in the country despite having only 1% of the world's vehicles. This study focuses on identifying accident blackspots in Bangalore and Chennai, two major metropolitan areas in South India, to enhance urban road safety and improve urban planning through targeted interventions. The research employs Quantum Geographical Information Systems (QGIS) for geospatial visualization and data analysis to identify high-risk zones and understand the underlying factors contributing to accidents. Data from municipal records and public datasets were cleansed and analyzed using machine learning techniques, including the K-nearest Neighbor Classifier for imputation of missing values. Key insights reveal specific accident-prone locations, speeds, months, days, and times when accidents are most frequent. These findings are visualized on detailed maps, aiding urban planners and policymakers in designing safer roads and improving infrastructure. This comprehensive approach, combining advanced data cleaning, machine learning, and geospatial visualization, provides actionable insights for targeted interventions to improve road safety in Bangalore and Chennai. Future research could extend this methodology to other Indian cities, incorporating real-time data for predictive analytics and broader applications.