Heart failure (HF) affects approximately 1% of the global adult population, with incidence expected to rise due to aging, leading to higher healthcare costs. The progressive nature of HF requires regular follow-up, precise medical therapy adjustments, and vigilant monitoring. Artificial intelligence (AI) and machine learning (ML) offer valuable support in managing HF by enhancing diagnostic and monitoring processes through their ability to process various types of data. This chapter explores AI and ML applications in HF management, including early diagnosis, risk stratification, and therapy optimization. AI has shown promise in improving diagnostic accuracy, predicting adverse events, and personalizing treatment, though further studies are needed to validate these benefits.