International Research Journal of Education and Technology

Accurate prediction of the Air Quality Index (AQI) plays a crucial role in minimizing the adverse effects of air pollution on human health and the environment. However, AQI prediction remains a complex task due to the highly non-linear, dynamic, and time-dependent behavior of atmospheric pollutants. Conventional statistical and machine learning approaches often fail to capture Experimental results demonstrate that the proposed LSTM model significantly outperforms traditional linear regression and Random Forest models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The findings indicate that the proposed system can function as a reliable early-warning tool for air quality monitoring, public health planning, and environmental policy formulation.  long-term temporal dependencies present in air quality data. This paper proposes a deep learning–based AQI prediction framework using Long Short-Term Memory (LSTM) networks to effectively  model  complex  temporal