Protein–protein interactions (PPIs) are essential for cellular functions such as signal transduction, immune responses, and metabolism. Experimental methods for detecting PPIs are typically costly, time-consuming, and limited in scale. Computational prediction methods offer an efficient alternative. Graph Neural Networks (GNNs) have emerged as a promising deep learning framework for modeling PPIs due to their ability to learn from graph-structured biological data. This paper explores GNN-based PPI prediction methods, reviews data representations and architectures, and demonstrates a computational workflow including molecular docking validation of tuberculosis-associated immune signaling proteins. Example docking analyses and Ramachandran plots illustrate the approach. The results support the use of GNNs for scalable and accurate PPI prediction to guide drug discovery and systems biology research.