<![CDATA[Air pollution is a critical environmental and public health concern, exacerbated by urbanization, industrial growth, and increased transportation. The air quality index (AQI) in major cities is significantly elevated due to rapid industrial expansion, fossil fuel consumption, and vehicular emissions. This study aims to predict AQIs using machine learning techniques, specifically integrating the Pelican optimization algorithm (POA) with the decision tree (DT) method to enhance accuracy. Data from prominent Indian cities—Mumbai, Delhi, Bangalore, Kolkata, and Chennai—was analyzed due to their high pollution levels. The model’s performance was validated against traditional machine learning methods such as k-nearest neighbors (KNN), random forest (RF) regression, and support vector regression (SVR). Results showed the highest prediction accuracies for Kolkata at 96.68%, followed by Bangalore at 95.66%, Chennai at 93.10%, Mumbai at 92.48%, and Delhi at 86.61%. These findings demonstrate that the proposed model outperforms conventional techniques in predicting AQI, providing a foundation for effective policy-making to mitigate air pollution impacts.]]>
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