Abstract: Air pollution remains one of the most pressing environmental challenges of the 21st century, it comes with severe consequences for both public health and ecological balance. Prolonged exposure to pollutants such as particulate matter, carbon dioxide, and volatile organic compounds has been linked to respiratory illnesses, cardiovascular diseases, and even premature mortality. Despite these risks, conventional air quality monitoring systems are often limited by high costs, fixed infrastructures, and restricted accessibility, leaving large populations without adequate real-time information. To address this gap, this study presents Vento Aureo, an IoT and Artificial Intelligence (AI)-based framework designed for real-time air quality monitoring and forecasting. The system leverages portable IoT sensors to collect pollutant data, which is later transmitted to the cloud for analysis. Machine learning algorithms are employed to identify patterns and predict short-term air quality trends, enabling proactive responses to hazardous conditions. Data visualization and user interaction are facilitated through a mobile application that delivers live readings and predictions directly to end-users, supporting informed decision-making in daily life. Furthermore, the framework holds potential to aid policymakers and urban planners by providing accessible, large-scale insights into pollution dynamics. By integrating portability, affordability, and predictive intelligence, Vento Aureo offers a practical step toward mitigating the harmful effects of poor air quality and promoting healthier urban environments.

Keywords : Air Quality Monitoring, Internet of Things (IoT), Machine Learning, Noise Pollution, Real-Time Data, Cloud Integration, Smart Environment.

Downloads: | DOI: 10.17148/IARJSET.2025.12811