Artificial Intelligence (AI) plays a crucial role in modeling currency correlations by analyzing vast amounts of financial data to identify complex relationships between currency pairs. Traditional correlation models rely on linear relationships, but AI-powered techniques, such as deep learning and reinforcement learning, can capture nonlinear dependencies and dynamic market shifts. Neural networks, particularly Long Short-Term Memory (LSTM) models, are effective in predicting how currency pairs move in relation to each other over time. These models process historical exchange rates, macroeconomic indicators, and geopolitical events to determine patterns that impact correlations. Additionally, AI techniques like clustering and dimensionality reduction help uncover hidden relationships that may not be apparent through traditional methods. AI-driven models adapt to changing market conditions faster than conventional statistical approaches, providing traders and financial institutions with more accurate and real-time insights. Despite challenges like overfitting and data quality issues, AI continues to enhance forex trading strategies, risk management, and portfolio diversification by refining currency correlation predictions.