Artificial intelligence (AI) is transforming the field of cardiac surgery by enhancing diagnostic precision, improving risk prediction, supporting intraoperative decision-making, and redefining surgical education. This chapter reviews the latest research and clinical applications of AI in cardiac surgery, focusing on preoperative planning, intraoperative support, and postoperative monitoring. Machine learning models have shown superior accuracy in predicting mortality and complications compared to conventional scores such as EuroSCORE II and STS. Intraoperatively, AI is being integrated into robotic systems and decision-support tools. Postoperative monitoring benefits from predictive algorithms that can identify various complications, such as atrial fibrillation, bleeding, or acute kidney injury, often earlier than traditional markers. The chapter also addresses the role of AI in surgical education, highlighting simulation platforms and automated feedback systems that offer scalable, personalized training. Despite these advances, several challenges persist, including data quality issues, limited external validation, and ethical concerns about transparency and accountability. The chapter concludes with perspectives on the future integration of AI in cardiac surgery, emphasizing the need for clinician oversight, multicenter validation, and targeted training in data science to ensure safe and equitable adoption of these technologies.