As urbanization and industrialization accelerate, the imperative for monitoring air quality intensifies due to its significant impact on health and the environment. This paper presents an innovative air pollution prediction model employing the adaptive multigrid LSTM (Long Short-Term Memory) algorithm. Our methodology begins with the preprocessing of a substantial dataset, comprising approximately 450,000 records across India, with a focus on 100,000 randomly drawn records. These records encompass 13 distinct features such as SO2, NO2, PM2.5, location etc. Through logistic gradient clustering for feature extraction, the data is prepared for subsequent analysis. The core of our approach, the adaptive multigrid LSTM algorithm, enables efficient and accurate prediction of air pollution levels. Evaluations indicate a significant improvement over conventional models, achieving an impressive accuracy of 93%. These metrics decisively outperform traditional techniques like AdaBoost, MLP, Gaussian NB, and SVM classifiers. This study demonstrates the feasibility of using advanced machine learning techniques for air quality forecasting, highlighting their potential for enhancing environmental health management and policy planning, especially in rapidly developing regions like India.