The functioning of the ecosystem and environment, as well as the lives and well-being of individuals in the immediate vicinity, are significantly impacted by air quality. People are able to make well-informed lifestyle decisions by forecasting the air quality index. The creation of an air quality prediction model was started, utilising a number of phases from data collection to model selection, including online scraping for dependability and rigorous data pre-processing and assessment. The primary work is to locate reliable meteorological data and handling erratic data (missing values and parameters, for example). Web scraping techniques and novel data prioritisation, including benchmark approaches like KNN Imputer and Power Transformer, are used to address these problems. The model that performed the best at predicting climatic quality among all the models that were assessed was the Bagging Regressor. Its optimum performance and ability to anticipate weather accurately are shown by a low least squares error (MSE) rating.