aspg-18-3293-1792025449 20261015005049 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 14 2 Ali Khraisat Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknika Mohd Khanapi Abd Ghani Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknika Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COVID-19 risk classifications, this study utilized three widely adopted forecasting models: support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and linear regression (LR). The findings reveal that applying these techniques in the current COVID-19 outbreak scenario is a viable approach. Results indicate that LR outperforms all other models tested in accurately forecasting death rates, recovery rates, and newly reported cases, with LASSO following closely. However, based on the available data, SVM exhibits lower performance across all predictive scenarios. 2025 165 177 3293 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.140214 https://americaspg.com/journal/18/article/3293 https://americaspg.com/storage/01731160579.pdf