In the face of mounting environmental concerns, air quality has become a pressing public health issue, necessitating advanced monitoring systems. This paper introduces R-TAMS, an innovative system designed to fulfil the need for precise air quality monitoring. Beyond real-time assessment, R-TAMS serves as a robust decision support tool for local authorities, navigating the intricate landscape of traffic emissions and environmental impacts in urban settings. Key functionalities encompass the real-time monitoring of diverse pollutants, including nitrogen oxides, carbon dioxide, carbon monoxide, hydrocarbons, and fine particles, originating from both exhaust and non-exhaust sources like brake and tire abrasion. R-TAMS also monitors real-time traffic noise and allows conducting prospective scenarios to evaluate the effectiveness of public policies, such as the establishment of low-emission zones and reduced speed limits areas. R-TAMS models were trained on data from real driving conditions, facilitating automated and replicable estimations of traffic flow and pollutant emissions. This replicability to any kind of territory allows automatic identification of critical areas that require priority interventions from the policy makers and local authorities. The paper further elucidates R-TAMS’ building blocks relying strongly on artificial intelligence, showcasing its innovative use for precise estimations, and contributing to a scalable solution adaptable to diverse local contexts. Future steps involve the seamless integration of atmospheric dispersion capabilities and air quality visualization maps, reinforcing R-TAMS as a holistic tool. This strategic expansion will provide comprehensive insights, enhancing its utility in addressing the urgent challenges posed by deteriorating air quality in contemporary urban environments.