Abstract
This review studies the state of air quality research in Kazakhstan, focusing on key pollution sources, health and environmental impacts, monitoring techniques, and regulatory frameworks. By analyzing recent studies, we identify emerging trends, research gaps, and methodological limitations. Special attention is given to the role of machine learning (ML) in overcoming data scarcity, improving predictive accuracy, and enhancing real-time air quality monitoring. These results emphasize the critical necessity of enhancing sensor networks, implementing data-informed policy measures, and adopting interdisciplinary strategies to address pollution effectively.