aspg-3-3448-1791419296 20261008002816 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 18 1 Elaf Adel Abbas College of Computer Science and Information Technology, University of Karbala, Karbala, Iraq Nisreen Abbas Hussein Babylon Education Directorate, Ministry of Education, Babil, Iraq Raaid Alubady Al-Ayen Iraqi University, Thi-Qar, Iraq; College of Information Technology, University of Babylon, Babil, Iraq Recently, the complex network has become popular use as it can transfer huge amounts of multimedia, text, ideas, and other information, encouraging many participant connections. Social media is one of these networks that make the most connections. Predicting the formation or dissolution of links between nodes presents a problem for social network analysis researchers. Since social networks are dynamic, this task is exciting as it may also forecast lost network links with less information. On the other way, current link prediction methods use simply node similarity to find links. This study proposes a new technique that relies on node attributes and similarity measures. Nodes are labeled by their centrality and similarity. The network's edges are negative and positive samples. A well-defined dataset for link prediction comprises the features of the nodes at the edges labeled either positive or negative. The dataset is passed to multiple machine learning classifiers. On several real-world networks. The experiments conducted during the research show that Gradient Boosting gave the highest accuracy of 99% compared with other methods. 2025 41 55 3448 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.180104 https://www.americaspg.com/journal/3/article/3448 https://www.americaspg.com/storage/81735521794.pdf