The growing urbanization, combined with increasingly strict air quality regulations, requires innovative approaches to manage air pollution, particularly from vehicular emissions. One of the most widely used methods to regulate air pollution in cities is the implementation of the so-called low emission zones (LEZs), areas of a city in which certain vehicles are restricted or their use is discouraged through economic fees. This paper proposes a comprehensive methodology for the implementation and management of low emission zones, integrating advanced air pollution dispersion models, traffic flow simulations, and AI-based video surveillance systems. The proposed approach uses the SUMO model for traffic simulation and the GRAL model for high-resolution pollutant dispersion, supported by meteorological data from the WRF and GRAMM models. The method ensures accurate delimitation of LEZs and enables effective monitoring of air quality both before and after its implementation. Additionally, the AI-based video systems provide real-time vehicle analytics and support traffic management within LEZs. The methodology was successfully applied in several European cities, demonstrating its adaptability and effectiveness. The results showed significant improvements in air quality predictions, thereby validating the proposed LEZs and contributing to the broader goals of urban air quality management.