The health and welfare of car occupants depend heavily on the quality of the air inside the cabin, especially in urban areas with high pollution levels. The Artificial Intelligence of Vehicular Things for Air (AIoVTA) system, which is intended to monitor and maintain air quality in car cabins in real time, is presented in this study. In order to identify and reduce dangerous pollutants like particulate matter (PM2.5), carbon dioxide (CO2), volatile organic compounds (VOCs), and other allergens that can build up in confined areas, the system combines air quality detection sensors, no of commuters, and artificial intelligence. AIoVTA continuously monitors the quality of the air within vehicles, adjusting ventilation systems and alerting passengers to any hazards by utilizing sophisticated. By limiting exposure to, the system not only promotes general wellness but also guarantees a comfortable environment. The air pollutants from outside are detected to a IoT device and simultaneously the levels of oxygen carbon dioxide and temperature sensor readings are parallelly taken in three scenarios like industrial area, highway and residential areas. The vehicle maintained a consistent speed. In the scenario of higher pollutants entering the cabin, the system alerted the commuters to enable recirculation mode in the industrial area. The regression algorithms as Extra Trees Regressor, Linear Regression, Support Vector Regressor, Elastic Net, CatBoost Regressor, Passive Aggressive Regressor, and Huber Regressor are calculated for the error rates on the real-time air data during different times of the day. The Extra Trees Regressor has given the least error value of 0.6492 and the air quality index was between 50 and 100.