The paper examines the application of the Graph Neural Networks (GNNs) to the ICEWS14 temporal knowledge graph to predict geopolitical events and relationship. We suggest a new architecture that involves negative sampling and temporal encoding trick that enhances performance on temporal link prediction. Our model combines a central temporal-conscious attention GNN with numerous domain-specific GNN sub-models that are taught on economics trends, political choices, and business interrelations and it additionally incorporates knowledge graphs, sentiment analysis of the people, and market tendencies data to provide comprehensive consulting assistance. The framework has an AUCROC of 0.9234, test set, indicating that the framework is more likely to detect both positive and negative links. Exploring the high-confidence predictions, we identify the regularities of the political enforcement action, whereas the low-confidence ones assist in suspecting the improbable or counterfeit connections. Such explanations are useful in designing early warning systems, risk-assessment monitors, and decision-support frameworks in foreign affairs and think tank consultancy.