This paper is based on real-time air quality classification, which is a basis for efficient air quality monitoring and public health management because poor air quality can cause severe health risks, including respiratory diseases, cardiovascular issues, and persistent health complications. This research has applied advanced machine learning techniques, such as Random Forest, Gradient Boosting Machines, and Linear Regression, to develop a reliable system for accurately classifying air quality levels. Using the proposed the whole set of data that incorporates different environmental factors, such as pollutant concentrations, meteorological conditions, and geographical data are analyzed. The proposed model has attained improved precision in assessing air quality. The system does real-time classification, along with giving actionable insights so that all stakeholders like public health officials and government agencies can make timely interventions in place. Only with better control and management of air quality through proactive public engagement, therefore, will everyone benefit from improved public health outcomes, along with a healthier environment for all.