Cardiovascular diseases (CVDs) continue to be the world’s leading cause of morbidity and mortality. Therefore, improvements in risk assessment, early identification, and treatment approaches are necessary. Despite their effectiveness, the traditional techniques face some negatives. The primary restrictions include late diagnosis, observer-dependent processes, resource-intensive nature, and so forth. Using machine learning (ML) and deep learning (DL) techniques, artificial intelligence (AI) has become a game-changing tool in cardiovascular medicine, improving disease detection, prognosis, and treatment. By eliciting minor patterns of diseases that conventional models usually miss, AI-driven models make more precise cardiovascular imaging-like interpretations from electrocardiograms (ECGs) and risk stratification. Furthermore, besides improved patient results and reduced hospital admissions, AI greatly enhances prediction analytics, real-time monitoring systems, and digital health technologies, enabling preventive interventions. Beyond diagnostics, AI is moving into every aspect of cardiovascular healthcare, including precision medicine, robotic-aided therapies, and drug discovery, all of which enhance the efficiency of the clinical process and improve treatment outcomes. Data privacy concerns, algorithmic bias, and legal framework limitations are among the obstacles that must be overcome to guarantee AI’s moral and just application.​ This chapter discusses the application of AI in cardiovascular medicine in detail, considering possible applications and implications, stressing the imperative need for interdisciplinary collaboration, regulatory frameworks, and technology development to maximize the transformative impact of AI on cardiovascular care.