Tehran University of Medical Sciences Journal of Air Pollution and Health 2476-3071 10 1 2025 03 03 1 18 Saif Saad Fakhrulddin Department of Biomedical Engineering, School of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, India AND Department of Biomedical Engineering, College of Dentistry, University of Mosul, Mosul, Iraq Vaibhav Bhatt Department of Biomedical Engineering, School of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, India Sadik Kamel Gharghan Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq 2024 11 12 2025 02 16 Introduction: Air pollution can trigger the attack in asthmatic patients if uncontrolled. Previous works focused on controlling pollution by proposing algorithms to predict air pollution. While these prediction algorithms save patients from attack triggers, they have limitations such as prediction accuracy, mathematical complexity, and lack of adequate patient notification systems. Materials and methods: This study proposed a novel Intelligent Air Pollution Prediction (IAPP) algorithm based on optimizing Random Forest Regression (RFR) to predict air pollution and send an alert message to the patient and hospital in real time. Meanwhile, IAPP utilized reliable data from Internet of Things (IoT)-based air pollution detection nodes. The performance of IAPP was evaluated in a real-world environment during the peak pollutant season to test the prediction accuracy of air pollution. Results: Results showed that the proposed IAPP achieved a high prediction accuracy of 99.98% with an R squared value of 0.99. This demonstrated that the IAPP algorithm based on the RFR model can effectively protect asthmatic patients from attack triggers. Conclusion: As a result, the IAPP algorithm reduces hospital visits during high pollution and enables patients to complete their daily activities without obstacles or absence. https://japh.tums.ac.ir/index.php/japh/article/view/855 https://japh.tums.ac.ir/index.php/japh/article/download/855/424