EcoGuard: AI-Driven Pollution Prediction and Optimization for Smart Cities

Environmental pollution has become one of the biggest problems for urban sustainability, human health, and climate stability. Rapid urban growth, industrial development, and increased transportation have led to a decline in air quality in modern cities. Traditional environmental monitoring systems mainly gather pollution data but do not offer the predictive insights and decision-making support needed for active environmental management. This paper introduces EcoGuard, an AI-driven framework for environmental monitoring and pollution prediction tailored for smart city settings. The system combines machine learning techniques with decision support to assess environmental factors like Air Quality Index (AQI), concentrations of particulate matter (PM2.5 and PM10), carbon dioxide levels, temperature, humidity, and traffic density. It employs a Random Forest-based predictive model to forecast pollution severity levels by using historical environmental data. Additionally, an optimization module identifies cost-effective ways to reduce pollution while considering environmental limits to help with sustainable urban planning. The system also includes an interactive visualization dashboard, allowing policymakers to track environmental conditions and analyse pollution trends in real time. Testing shows that the proposed framework achieves a prediction accuracy of 92%, surpassing other tested machine learning models, including Support Vector Machines and Decision Trees. The results suggest that the EcoGuard framework offers reliable pollution predictions and useful decision-making support for environmental management in smart cities.