aspg-28-3506-1792153580 20261016122620 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 9 1 K. Selvi Professor, Department of IT, Paavai Engineering College, Namakkal, Tamil Nadu, India K. Muthumanickam Professor, Department of IT, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India P. Vijayalakshmi Associate Professor, Department of CSE, Knowledge Institute of Technology, Salem, Tamil Nadu, India S. Sakthivel Assistant Professor, Department of CSE, Arulmigu Arthanareeswarar Arts and Science College, Tiruchengode, Tamilnadu, India COVID-19, one of the most highly transmissible diseases in the twenty-first century, has had a profound impact on global lifestyles. Recently, the medical industry has increasingly relied on machine learning, which shows promise in anticipating the presence of COVID-19. By using machine learning techniques, test result turnaround time can be accelerated, and medical personnel can promptly attend to patients’ needs. These algorithms analyze various attributes to classify COVID patients and predict their likelihood of contracting the disease. This study aims to utilize X-ray images processed by machine learning algorithms to predict the occurrence of COVID-19 and enhance its detection rate. The paper outlines two strategies employing machine learning techniques: one for predicting the likelihood of infection and the other for identifying positive cases. Different machine learning algorithms, such as decision trees, logistic regression, support vector machines, naive Bayes, and artificial neural networks, were employed. The simulation results reveal that the artificial neural networks model outperforms other methods in terms of accuracy rate. 2025 01 07 3506 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.090101 https://www.americaspg.com/journal/28/article/3506 https://www.americaspg.com/storage/articles/manuscripts/01M4Y381BMAG838KXGM4ZB52VH.pdf