<p>Identifying influential nodes in complex networks is a fundamental challenge in network analysis, as these nodes often regulate information spreading and structural robustness. While node centrality methods have been widely used for identifying such nodes, they are often tailored to specific problems. In this research work, a novel method InfVIKOR is proposed which aims to accurately identifying influential nodes in complex networks. This method utilizes Multi-Criteria Decision Making (MCDM) approach called VIKOR, which integrates four centrality measures into a unified group utility score. It leverages the VIKOR compromise ranking principle to balance group utility and individual regret. Compared with conventional MCDM techniques such as TOPSIS and AHP, InfVIKOR achieves higher consistency, reduced computational complexity, and rankings that better align with real diffusion dynamics. The node rankings are generated using the quick sort algorithm applied to the group utility function. To validate the effectiveness of proposed method, the SI simulation model demonstrate superior accuracy and scalability on six real-world networks. InfVIKOR thus offers a robust, efficient, and generalizable framework for identifying influential nodes in complex and scale-free networks.</p>