One of the most important problems facing the entire planet is air pollution. There are various pollutants in the air that degrade the air and create a hazardous environment. This study analyses these contaminants and uses the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model to predict them. One time series analysis model that predicts specific values based on past data is the SARIMA model. The data set utilised in this model includes various pollution values that were seen at a particular time and place. When the SARIMA model was used on the data set, the pollutants could be predicted. It is a reliable method for determining whether pollution values are higher than the World Health Organisation (WHO)-mandated limits. As a result, it raises public and governmental awareness so that appropriate steps can be made to reduce the levels of such dangerous pollutants. On the basis of the provided data set, this technique's efficacy is examined, and its performance is evaluated.

air pollution, SARIMA, prediction, dataset, awareness.