The Air Quality Index (AQI) is an air quality standards indicator based on air pollutants that have negative impacts on human health and the environment. Because of several human activities, air pollution is growing very quickly, and it is the introduction of chemicals, particulate matter or biological materials into the atmosphere that cause human suffering and also harms the natural environment. Indeed, air pollution in metropolitan and industrial cities is one of the major environmental problems. So predicting pollution and avoiding these issues is very crucial. One of the most exciting and difficult functions is the forecast of air pollution using data mining. Many systems are designed to help data storage, inventory management and convenient statistics generation. India's air quality indicator is a standard measure used to indicate pollution (so2, no2, rspm, spm, etc.) from time to time. The main purpose of the current study is to predict the temporal AQI used by the previous day AQI and climate change is used to predict and visualize the temporary data mine using a gradient break and an unreasonable forecasting process. In Navigation Forecast, we divide the database into 85% data and 15% data based on data testing and training to determine seasonal variations and styles. Balance problems are often exploited by problems and forecasting uses an unreasonable prediction process and gradient downtime. Air quality forecasts based on historical data of previous years and predictions for less than a year as a reputable gradient using a recurring problem.