This study examines the problem of urban traffic congestion and safety. An integrated approach for designing an adaptive traffic signal timing (TST) while modeling pedestrian dynamics is presented to achieve this. The increased difficulty of vehicle circulation and pedestrian protection in crowded city quarters requires modern tools such as instant data processing and advanced modeling approaches. Research literature on traffic control techniques is used to review the existing studies and methods. The study gaps are highlighted, and the chances of improving them are indicated. Our methodology involves several key steps: gathering and analyzing data; modeling, simulating, and validating; performing in situ test runs; continuously calibrating and enhancing, and systematically addressing and communicating the results. We utilize a combination of statistical techniques, machine learning algorithms, and simulation experiments to investigate the suitability of our TST optimization models and their impact on traffic flow and pedestrians’ safety. Results reveal a considerable decline in vehicles’ susceptibility to delays, queue length, and pedestrians’ wait times, which contribute to optimal traffic conditions and movement. Sensitivity tests reveal that our outputs remain valid for diverse values of input parameters, and ethics considerations support our integrity and impartiality in research. Thus, we serve the cause of developing transportation engineering and urban planning for cities, providing practical situations and recommendations for policymakers, urban planners, and researchers. Therefore, this article is an extensive guide to urban traffic management with safety issues, thus creating an excellent opportunity to attain a more efficient and sustainable urban transportation model.