The article addresses the growing challenge of analyzing the cryptographic key predictability in the era of the high-tech artificial intelligence and data-driven cybersecurity. Computational infeasible Classical cryptanalysis (like brute-force, statistical models) lacks a model of rich relational interactions among cryptographic processes. The study will overcome them by proposing a new Graph Neural Network (GNN)-based model which views the cryptographic key generation as a graph-structured data, and the latent structural patterns can be mined. The methodology involves the combination of graph construction, feature embedding and multi-architecture GNN learning (GCN, GAT, GraphSAGE) and optimized training strategies. Experimental evaluation of synthetic and benchmark data results indicate high performance with 89.3 percent accuracy, 0.88 precision, 0.87 recall, and 0.89 bits entropy reduction, which is better than statistical, machine learning and deep learning baselines. The proposed model is described as having a higher prediction capacity and a lower computational cost (38 ms inference time). The publication contributes to a systematic analysis of cryptography and offers novel insights into how to detect the leakage of entropy and design secure systems.