Advanced machine learning approaches, especially Long Short-Term Memory models, are used in the field of air quality forecasting. The dataset is organized to capture the concentrations of several pollutants gasses as factors used to determine the Air Quality Index. After a thorough comparison analysis of the dataset with other models such as K-nearest neighbor, gradient booster, linear regression, decision tree, Support Vector Machine and Long Short-Term Memory, it is shown that Long Short-Term Memory is the most effective in providing increased predicted accuracy. The project also has a strong security mechanism that includes login and user registration capabilities, strengthened by the generation of OTP using email authentication. By allowing users to enter important parameters such as the name of the city, the country, and the goal date for AQI prognostication, the user interface simplifies the procedure. Then, with ease, the interface smoothly displays the predicted AQI value for the given date, improving usage and accessibility.