Integration of artificial intelligence (AI) into air pollution monitoring systems has brought about a transformative change in the way we observe and understand environmental conditions. Unlike traditional monitoring methods, which often struggle with the nonlinear and complex nature of air pollutants, AI-driven techniques offer more accurate, timely, and comprehensive insights. This chapter delves into the vital role of various AI methodologies, particularly machine learning (ML), deep learning (DL), and neural networks, in the assessment, prediction, and real-time monitoring of air quality. Conventional approaches to monitoring air pollution have been limited by their inability to handle dynamic and multifaceted pollutant behaviors. However, artificial intelligence has shown significant promise in identifying intricate spatial and temporal patterns in pollution data. Leveraging data from ground-based sensors and satellite imagery, AI models facilitate real-time tracking of air pollutants and enable early warning systems for rising pollution levels. By enhancing data processing capabilities, AI not only improves the precision of air quality assessments, but also reduces operational costs through automation, optimization, and anomaly detection. This, in turn, supports sustainable urban development by equipping policymakers with data-driven insights and promotes faster responses to environmental health risks. This chapter aims to review the key advancements in AI applications for air quality monitoring, explore the challenges that remain, and highlight future directions. Emphasis is placed on enhancing system efficiency, data reliability, and regulatory compliance to pay the way for smarter, more sustainable management solutions.