Abstract
Air pollution causes approximately 7 million premature deaths annually, highlighting the urgent need for advanced monitoring solutions. This study presents an Intelligent System that integrates IoT-based sensors, cloud platforms, and machine learning algorithms to monitor key pollutants, including fine particulate matter (PM2.5), nitrogen dioxide (NO2), and carbon dioxide ($\mathbf{C O}_{\mathbf{2}}$). The system, built on a modular and scalable platform using an ESP32-based board, enables real-time pollution detection and forecasting. It employs a Random Forest model with 89% classification accuracy, providing precise insights into environmental conditions. Interactive monitoring dashboards and predictive analytics enhance decision-making, helping policymakers, researchers, and local communities respond promptly to changes in air quality. By bridging data and decisionmaking, this platform improves air quality management, contributing to the development of sustainable urban and industrial ecosystems for a healthier future.