This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.990 and a recall of 0.872. This performance is attained with minimal hyperparameter tuning, making it scalable for large-scale material discovery applications. Data augmentation, including synthetic data generation and noise injection, enhances the model's robustness by simulating real-world experimental variations. However, the model's reliance on synthetic data and the computational cost of graph construction and inference remain limitations. Future work will focus on integrating real experimental data, optimizing computational efficiency, and exploring lightweight architectures to improve scalability for high-throughput applications.