Ramsey Theory is a strong tool of combinatorics of studying the patterns that will inevitably occur in complex networks and is of great importance in network security. The current paper introduces an analytical framework based on Ramsey, which can be used to identify the latent patterns, anomalies and adversarial structure of communication graphs. The solution offered includes machine learning models that have graph-based algorithms to detect and predict threats adaptively. The framework determines worst-case attack scenarios, and structural vulnerabilities by modeling the spread of vulnerabilities with Ramsey-critical subgraph. Experimental analysis shows that the detection (up to 94.6%) and false alarms are lower compared to the traditional graph algorithms. The limitations of the computational complexity and the issue of scalability, which provides optimized hybrid solutions are also discussed in the paper.