This study delves into the forefront of air quality management by presenting a unique research-centric investigation into the integration of automated machine learning (ML) techniques. Departing from conventional methodologies, the research focuses on developing a sophisticated system that utilizes automated ML algorithms to streamline the monitoring and regulation of air quality. Through rigorous experimentation and validation, the study aims not only to enhance the accuracy and efficiency of air quality management but also to contribute valuable insights into the optimization of ML algorithms for environmental monitoring applications. The innovative framework proposed in this study holds the accuracy of 98% revolutionizing current practices, offering a scalable and adaptable solution for addressing the multifaceted challenges of modern air quality governance. By employing a research-centric approach, the study emphasizes the exploration of novel ensemble model with Random Forest and Decision Tree classifier tailored specifically for the complexities of air quality data analysis. The techniques developed through in-depth investigation of advanced ML methods can potentially be extended to other environmental regulation domains as well.