This paper proposes an innovative approach to improve the accuracy of air quality index (AQI) prediction by integrating human–machine collaboration and random forest regression (RFR) model. Machine learning (ML) is central to predictive analytics, but its ever-changing complexity presents challenges in areas such as air quality. To address this problem, our approach leverages human involvement strategies to improve the reliability and accuracy of machine learning predictions to evaluate learning. When machine learning reliability drops below a dynamically determined threshold and uncertainty arises, law enforcement’s ability to intervene manually is transferred to human experts. This collaborative technique provides higher prediction accuracy without human intervention compared to the original model. It also provides a feedback loop to continually improve the initial model through continuous learning. We focus on a historical air pollution dataset for Jaipur, Rajasthan, covering the year 2023. Random forest regression was chosen for its ability to detect non-linear relationships in air quality data. By combining human knowledge and computer models, our approach overcomes the limitations of fully automated forecasting systems and leverages the power of human reasoning and machine learning to improve the accuracy of AQI forecasts. This research contributes to the understanding and application of AI–human interactions in predicting the environment and its potential impacts on health and nature.