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Accurate traffic classification plays a pivotal role in the management and optimization of modern computer networks. Traditional machine learning methods have shown promise, but they often fail to capture the complex and dynamic nature of network traffic. This paper proposes the use of Graph Neural Networks (GNNs) for traffic classification in large-scale networks, leveraging the relational information among flows and nodes. Our method models traffic data as a graph, with flows and their metadata represented as nodes and edges. The GNN learns from these interactions to classify traffic into predefined categories. Experimental results demonstrate that our GNN-based approach significantly outperforms baseline models in terms of accuracy and generalization across varied datasets.