Air pollution, in general, implies to discharge of impurities into the atmosphere which is harmful to human life and the in-universe. It has the potential to be the most destructive menace humanity has always faced. It gives problems to animals, birds, yield, crops, and forests among other things. To overcome the difficulty, machine learning techniques must be used to anticipate air quality from pollutants. As a result, forecasting the quality of air monitoring and prediction of it has been a significant study area. The actual objective is looking for machine learning-based solutions for forecasting air quality with the highest level of accuracy. The entire dataset will be analyzed using machine learning techniques to calculate large bits of data which are variable identification, univariate analysis, and multi- variate analysis, also bi-variate analysis, missing value treatments, and data validation, data cleaning/preparing, and visualization. Our paper offers a detailed guide to model parameter risk assessment in terms of performance in forecasting air quality pollution calculating prediction accuracy. For presenting a technique using machine learning for reliably predicting the Air Quality Index value by comparing various classifying machine learning methods and producing calculation results in the type of highest accuracy. In addition, the outputs of several machine learning methods from the provided datasets will be compared and discussed, along with an estimation of the user interface which is GUI-based for forecasting air quality using attributes present in the datasets.