Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4122 2022 2022 Al-Furat Al-Awsat Technical University, Technical Institute of Najaf, Najaf, Iraq Noor Noor Sciences of Mathematics, Computer Sciences, College of Health and Medical Techniques-Kufa, Al-Furat Al-Awsat Technical University, Kufa, Iraq Ghassan AL .. Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey Isam Bahaa Aldallal Engineering School of Digital Technologies, Yugra State University, Khanty-Mansiysk, 628012, Russia Mostafa Abotaleb Department of Production and Management, Polytechnic University of Tirana, 1001, Tirana, Albania Klodian Dhoska Hierarchical clustering is applied in this research to study world COVID-19 data up to January 2025 and partition the primary clusters of countries based on epidemiological criteria. Total cases, deaths, recoveries, active cases, tests, population, and per-million were the data explored and were standardized and thereafter analyzed employing agglomerative hierarchical clustering with Ward linkage. The assessment yielded an average Silhouette of 38.5%, Davies–Bouldin value of 0.87, and Calinski–Harabasz value of 77.6, reflecting cluster validity in separation. The application of dendrograms and PCA projections to plot identified four clusters, reflecting differences in the severity of COVID-19 impacts and responses. Clustering analysis revealed that the high-burden clusters accounted for almost 45% of global death, while low-burden clusters were predominant in over 40% of nations with fewer than 100,000 accumulated instances. The outcomes illustrate hierarchical clustering as an unsupervised learning approach to analyzing epidemiological data and give quantitative estimates to facilitate comparative public health interventions across communities. 2025 2025 20 31 10.54216/JAIM.100202 https://www.americaspg.com/articleinfo/28/show/4122