<p>This paper proposes a novel nonlinear conjugate gradient method for addressing large-scale unconstrained vector optimization problems. The proposed approach represents a non-trivial extension of the classical scalar-valued nonlinear conjugate gradient framework to the vector optimization setting. A new non-negative conjugate parameter is introduced, based on which the corresponding search direction is rigorously proven to satisfy the sufficient descent condition without relying on any line search procedure. Under the standard Wolfe line search conditions, global convergence of the newly developed nonlinear conjugate gradient method is established, without requiring the convexity assumption on the objective functions. Finally, comprehensive numerical experiments are conducted to demonstrate the practical performance and efficiency of the proposed method.</p>