This research examines how ICICI Bank uses AI to assess credit risk. The study analyzes structured and unstructured data to see how artificial intelligence models improve borrower creditworthiness. Machine learning predicts default likelihood and reduces non-performing assets using decision trees, neural networks, and ensemble approaches. The project investigates adding AI to credit scoring systems to increase their efficiency and accuracy. Real-time data processing enables dynamic risk monitoring, enabling institutions to quickly address financial difficulties. ICICI Bank’s AI-driven credit models decrease bias and speed decision-making. This study evaluates qualitative borrower data using natural language processing. Strategic lending and portfolio optimization are made easier using predictive analytics. Critical studies examine regulatory compliance, data privacy, and model interpretability. This study analyzes the cost-benefit of AI in credit operations.