Air pollution detection technologies have advanced significantly over the years; however, several gaps and limitations persist within the current methodologies. The chapter begins by briefly addressing the effects of air pollution on public health and the environment. Then it investigates the existing challenges faced by the air quality assessment system, which may arise from economic, geographical, systemic, and procedural factors. Special attention is directed toward the challenges involved in identifying localized pollution, which are made worse by sporadic monitoring, unreliable information, and the insufficient placement of traditional monitoring stations, especially in rural areas. The chapter also explores methods based on artificial intelligence, such as machine learning and predictive analytics, and their contribution to improving the effectiveness of air quality evaluations. It consists of case studies that demonstrate how some existing and introduced technologies are used to predict pollution levels, employing data forecasting methods including long short-term memory and convolutional neural network models. Additionally, the chapter supports the use of artificial intelligence to reduce and monitor unexpected changes in air quality index measurements, which may result in overall imprecision. It suggests combining sensor information collected using different sensor types, such as electrochemical sensors and optical particle counters, with simulation models designed to efficiently increase public awareness. In summary, the chapter highlights the advantages of using artificial intelligence in air quality monitoring systems, suggesting that this combination could be a significant step toward obtaining accurate data and promoting a cleaner future.