The pernicious consequences of urbanization and the encroachment upon transitional ecosystems in India are manifold. Unpredictable weather, unpredictable precipitation, a relentless climb in sea levels, and the destruction of valuable ecosystems and complex food webs are all symptoms of these negative impacts. It is critical that parliamentarians, non-profits, and governments throughout the world work together to address the pressing need to determine what is causing climate change (CC) and how to mitigate its effects. From September to December 2023, climate trends were examined in different Indian states at daily mean intervals for this work. In order to scrutinize the primary issue with CC, the whole investigation is centered on temperature and air-quality metrics CO, NO2, SO2, and O3. Several machine learning (ML) models were trained and evaluated on the dataset to get the enhanced prediction accuracy observed in the suggested technique. As part of EDA, the suggested random forest regression (RFR) model achieved an accuracy of 94.67%, whereas the other models MLR, Lasso regression, DTR, and GBR achieved accuracies of 69.53%, 68.13%, 88.14%, and 92.04%, respectively.