The detection and ranking of influential nodes remains one of the key areas of research for understanding information diffusion, epidemic control, routing efficiency, and online influence in large-scale complex networks. Centrality measures have been proven to be the most reliable methods that effectively capture the node’s influence in the literature. Based on the structural information incorporated, these measures can be classified as local centrality (PageRank, degree, etc.) and global centrality (betweenness, closeness, etc.) measures. Nevertheless, global measures require huge computational resources in large-scale networks, whereas local measures suffer with less accuracy. To address these challenges, this work proposes a convex combination-based hybrid centrality method. Leveraging the proposed method, we design the six novel centrality metrics, namely convex isolating betweenness centrality (CIBC), convex isolating clustering coefficient centrality (CICLC), convex isolating coreness score centrality (CICRS), convex isolating degree centrality (CIDC), convex isolating eigenvector centrality (CIEC), and convex isolating Katz centrality (CIKC). Next, we compare the effectiveness and computational efficiency of the proposed measures with the traditional and recent measures on the SIR (susceptible–infected–recovered) model using real-world network datasets. Our comprehensive simulations validate the proposed convex centrality measures, showing enhanced spreading efficiency and modest improvements in time complexity.