Air quality prediction is crucial for public health and environmental management to minimize the harmful effects of poor air quality. This study proposes a novel Air Quality Prediction System (AQPS) that leverages ensemble learning techniques to improve the accuracy and reliability of air quality forecasts. The proposed method uses different ensemble models such as Random Forest, XGBoost, CatBoost and AdaBoost. This ensemble learning-based system shows enhanced predictive performance and dependability when compared to standard single-model techniques. The system is trained using an extensive dataset that includes historical, atmospheric, and meteorological data on air quality, providing it a thorough understanding of the factors affecting air quality. Using real-world air quality metrics, thorough experiments have been carried out to assess the efficacy of the proposed AQPS. Not only does the suggested AQPS perform better than standalone models, but it also shows improved generalization in many regions and atmospheric conditions. In the final analysis, the holistic approach helps with the efficient management of public health and air quality by utilizing the abilities of many models to improve precision and robustness.