Link prediction in complex networks aims to infer missing or future connections between nodes, a task crucial for understanding network evolution in domains such as social systems, biology, and recommender platforms. Traditional similarity-based approaches, which rely primarily on local neighborhood overlap, often fail to capture global structural properties and node importance, resulting in limited predictive accuracy in sparse or large-scale networks. To address these limitations, we propose a Link Prediciton based Centrality using Graph Neural Network (LP-GNN) framework that integrates centrality measures with similarity indices in a unified, parameterized learning paradigm. The proposed LP-GNN operates in three sequential stages. 1) Feature Construction: Edge features are enriched by a weighted fusion of structural similarity and node centrality measures, providing a comprehensive representation of both local and global graph information. 2) Centrality-Aware Encoder: A modified message-passing mechanism is employed to learn node embeddings, where the influence of structurally important nodes is amplified, ensuring that critical topological information is effectively captured. 3) Multi-Branch Decoder: Multiple complementary scoring mechanisms are integrated including dot-product similarity, a multilayer perceptron (MLP) to model non-linear interactions, and distance-based measures to generate robust estimates of link probabilities between node pairs. Our approach was evaluated across multiple real-world benchmark datasets, demonstrating its superiority over both traditional similarity-based methods and recent GNN-based models. Notably, it achieves improvements of up to 49% in Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPR) over state-of-the-art baselines. The highlight the effectiveness of combining structural interpretability from centrality with the representational power of GNNs, offering a robust and scalable solution to the link prediction problem in complex networks.