The research methodology involves the collection of data using CCTV cameras, and the Google Maps API to analyze traffic patterns and density at intersections. The system dynamically adjusts traffic light timings based on real-time traffic conditions, harnessing the power of the Google API for enhanced accuracy and efficiency. The ATLMS (Adaptive Traffic Light Management System) demonstrates significant improvements in traffic management by reducing waiting times, enhancing traffic flow, and minimizing delays at intersections. Notably, the system considers traffic density as a critical factor in traffic light timing adjustments, leading to optimized traffic movement. Additionally, the intelligent components facilitate efficient emergency vehicle prioritization and improve pedestrian safety through the implementation of smart crosswalk systems. The findings underscore the significance of ATLMS which integrate traffic density analysis, the Google API, emergency vehicle management, and pedestrian safety measures. The ATLMS offers a scalable solution for various intersection types and configurations, effectively addressing the unique traffic challenges of urban areas. By considering traffic density, incorporating accurate data from the Google API, prioritizing emergency vehicles, and enhancing pedestrian safety, the system provides a comprehensive solution for efficient traffic management. Future research endeavors may explore further enhancements and the integration of advanced technologies to meet the evolving traffic demands of urban areas.