Artificial Intelligence has fundamentally reshaped financial forecasting and risk assessment in global markets by integrating data-driven learning algorithms with predictive analytics, high-frequency trading, and real-time portfolio optimization. In an era characterized by market volatility, geopolitical uncertainty, and rapid technological change, traditional econometric and statistical models are no longer sufficient to capture the nonlinear and dynamic relationships among financial variables. AI-driven systems—encompassing machine learning, deep learning, natural language processing, and reinforcement learning—enable financial institutions to uncover hidden patterns within massive datasets and to generate more accurate, adaptive, and timely forecasts. These intelligent systems analyze structured data such as prices, volumes, and interest rates alongside unstructured data like news sentiment, social-media tone, and macroeconomic narratives to construct holistic market intelligence. The global financial ecosystem now relies on algorithmic models capable of continuously learning from new data, improving predictive precision, and detecting early signals of systemic risk. AI has become indispensable for asset-price forecasting, credit scoring, fraud detection, and portfolio risk management. Major banks, hedge funds, and central institutions deploy neural networks and ensemble models to forecast exchange-rate fluctuations, commodity price movements, and sovereign-risk exposures across interconnected economies. As financial systems digitalize, AI contributes not only to efficiency but also to resilience by supporting regulatory compliance, early-warning systems, and stress-testing mechanisms. This paper explores how AI redefines forecasting accuracy, enhances financial stability, and mitigates risk in the context of globalized markets that demand both speed and transparency.