As urbanization and industrialization continue to rise, air quality has become a pressing concern affecting public health and the environment. This paper presents a comprehensive approach to real-time air quality prediction utilizing advanced machine learning (ML) techniques. By leveraging a rich dataset of air quality parameters, meteorological factors, and temporal variables, we develop predictive models that accurately forecast air pollution levels in various urban settings.The proposed methodology employs several cutting-edge ML algorithms, including Random Forest, Gradient Boosting, and Deep Learning models, to analyze historical air quality data and identify patterns that influence pollutant concentrations. Through rigorous feature selection and model optimization, we enhance the predictive accuracy of our models while ensuring computational efficiency for real-time applications.Our results demonstrate that the advanced ML models significantly outperform traditional statistical methods in predicting air quality indices, providing actionable insights for policymakers and stakeholders. The integration of real-time data from IoT sensors further enhances the models' responsiveness, allowing for dynamic updates and improved prediction reliability.By providing a robust framework for real-time air quality prediction, this research contributes to the development of effective environmental monitoring systems, enabling timely interventions to mitigate air pollution impacts. Ultimately, our findings underscore the potential of advanced machine learning techniques in addressing critical environmental challenges and fostering healthier urban living conditions.

Air Quality, Random forest, Decision tree, Prediction, Real-time monitoring, Forecasts.