Air pollution is a significant global environmental challenge that poses serious risks to human health. According to recent studies it is evident that this impact goes beyond the physiological factors such as respiratory and cardiovascular disorders and affects cognitive functions, which were comprehensively analyzed in this paper to highlight the importance of mitigation strategies and develop public health policies. The proposed theoretical framework aims to combine principles from cognitive neuroscience, environmental health, and artificial intelligence to enhance our understanding and prediction of cognitive outcomes of air pollution exposure. To better analyze the complex relation between exposure to air pollution and variations in cognitive function of exposed individuals AI methodologies and machine learning models can be utilized. This study aims at bridging the gap in the literature by theoretically exploring the suitable ML models for understanding the relation between cognitive performance and exposure to air pollution. By combining insights from environmental science, public health, and AI, this research seeks to identify the potential cognitive outcomes of air pollution exposure for mitigating its adverse effects, building informed policies and provide quality life.