Network science is a domain in which we focus on studying complex networks like social networks, chemical networks, computer networks, telecommunication networks, cognitive networks, semantic networks, and biological networks. In recent years, link prediction in complex networks has become an active research field because of its various real-world applications. In this paper, we present a novel algorithm for link prediction influenced by the concept of vertex entropy and ego networks. We used 12 real-world datasets to evaluate the performance of the novel algorithm. Results are compared with the 12 baseline algorithm based on 4 metrics AUC, Precision, Prediction-Power, and Precision@K. Experimental results show the effectiveness of the novel algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.