The project aims to address the critical issue of urban air pollution caused by vehicular emissions. By integrating Machine Learning (ML) and Internet of Things (IoT) technologies, this project seeks to develop a real-time monitoring and control system to mitigate vehicular pollution. IoT sensors will be deployed on vehicles and in strategic urban locations to collect real-time data on pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM). This data will be analyzed using advanced ML algorithms to identify patterns, predict pollution levels, and recommend actionable measures to reduce emissions. The project will also incorporate engine model and fuel quality data to provide a comprehensive analysis of emission and traffic-related pollution. The expected outcome is an intelligent, data-driven system capable of providing timely insights and interventions, thereby contributing to cleaner air and healthier urban environments. This approach not only enhances pollution monitoring and control but also supports sustainable urban planning and public health initiatives.