Over the years, predicting and analyzing air quality has undergone significant advancements. In the past, we heavily relied on traditional methods like statistical models and simplified equations. However, these approaches struggled to capture the complex and dynamic nature of air pollution. As technology evolved, scientists and researchers turned to AI, machine learning, and big data analytics to improve air quality predictions. On the other hand, air pollution is a critical global issue that affects not only our environment but also our health and well-being. It is also linked to respiratory and cardiovascular diseases, leading to an increase in illnesses and deaths. Accurate air quality predictions empower governments, local authorities, and individuals to take timely actions to combat pollution, safeguard public health, and optimize urban planning. To tackle this pressing problem, we need accurate air quality prediction and analysis. Our motivation behind developing this AI model stems from the limitations of traditional air quality prediction methods. We've seen that these methods often lack accuracy and struggle to account for the intricate factors influencing air pollution. The potential of AI, with its ability to process vast amounts of real-time data and identify complex patterns, offers a promising solution to enhance the accuracy and reliability of air quality predictions. Therefore, this work introduces an innovative Artificial Intelligence (AI) model designed to predict and analyze air quality with exceptional precision and efficiency. By incorporating cutting-edge AI algorithms and data analytics techniques, this model aims to meet the growing demand for reliable real-time air quality information