The cardiac intensive care unit (CICU) is a critical environment where patient conditions can rapidly evolve, requiring continuous and complex data monitoring and interpretation. Artificial intelligence (AI) and machine learning (ML) offer significant potential to support physicians and nurses in analyzing the data on vital parameters and for assessing illness severity and estimating mortality risk. However, critical care presents unique challenges and opportunities, such as the intertwining of patient features and comorbidities, constantly evolving clinical scenarios, and ethical considerations, which can limit the application and effectiveness of AI tools. Despite these challenges, AI has shown promises in several areas within the CICU. AI and ML models have been developed to predict the onset and worsening of cardiogenic shock, stratify patients into phenotypic clusters, estimate mortality risk, and anticipate clinical deterioration, such as hypotensive episodes or systemic hypoperfusion. These applications have demonstrated varying degrees of success, often outperforming traditional risk scores, though their generalizability is sometimes limited by inconsistent definitions and heterogenous patient populations. Moreover, AI has been applied to optimize therapy in CICU patients, including mechanical circulatory support and ventilation strategies. AI-driven models have enhanced patient selection for mechanical support, predicted outcomes, and identified complications. Additionally, AI has been used to personalize ventilation strategies, predict the need for prolonged respiratory support, and anticipate cardiac arrest. However, while these advancements highlight the potential of AI in critical care, the real-world implementation and validation of these tools remain in their early stages, with limited evidence demonstrating significant improvements in patient outcomes. This chapter explores the major applications and limitations of AI in CICUs, emphasizing opportunities for enhancing patient care and outcomes through personalized treatment strategies and advanced risk stratification.