<![CDATA[The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was conducted, utilizing databases such as Professor, Scholar, Embl, and Nih to identify relevant studies on air pollution epidemiology. The review highlights the integration of data mining, machine learning, and spatiotemporal modeling to improve the detection, analysis, and forecasting of air pollution-related health issues. The findings reveal a growing trend in applying data mining techniques within the field of air pollution epidemiology. Advanced methods, such as spatiotemporal analysis and geographic data mining, enable more precise tracking and forecasting of pollution-related health risks. Continuous advancements in artificial intelligence and the development of more sophisticated sensors and data storage technologies are enhancing the accuracy and reliability of air quality monitoring and public health predictions. This study highlights the transformative potential of integrating data mining and AI techniques into air pollution epidemiology. Exploring emerging technologies like spatiotemporal mining and next-generation sensors paves the way for more accurate, timely, and scalable solutions to monitor air quality and predict its impact on public health, opening new avenues for research and policy interventions.]]>
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