Numerous artificial intelligence (AI) and machine learning (ML) models have been developed in recent years. These technologies have the potential to improve biomedical data analysis, particularly in risk stratification and mortality prediction. AI excels in categorizing patients based on their likelihood of adverse outcomes and estimating death risk, guiding clinical decisions and possibly improving patient outcomes. AI’s ability to learn from diverse data types and autonomously select relevant variables enhances its predictive power compared to traditional methods. This chapter explores AI’s current applications in risk stratification and mortality prediction, highlighting its successes and challenges. Notably, AI’s capacity to analyze ECGs, CT scans, and free-text data demonstrates its versatility and potential for improving patient care. Despite promising results, further validation is necessary for AI’s widespread implementation in clinical practice.