<p>Hypertension, or high blood pressure (HBP), is a widespread chronic condition that significantly elevates the risk of cardiovascular diseases and remains a leading cause of preventable mortality and disability-adjusted life years (DALYs) globally. Despite advancements in understanding its pathophysiology and treatment, hypertension continues to pose a substantial public health challenge. The rapid development of artificial intelligence (AI) has introduced new opportunities for enhancing hypertension management. AI’s robust data processing and pattern recognition capabilities hold great promise in improving hypertension diagnosis, developing personalized treatment plans, and promoting patient self-management. These advancements are expected to enhance the efficiency and effectiveness of interventions, thereby reducing the public health burden of hypertension. However, challenges such as data privacy protection, algorithmic bias, model accuracy, and the feasibility of clinical implementation remain to be addressed. This comprehensive review summarizes recent progress in the application of AI in hypertension management, highlighting its potential to revolutionize diagnosis, optimize treatment strategies, and support patient self-management. Unlike previous reviews, this study provides a more in-depth analysis of the latest AI technologies, including cuffless blood pressure monitoring and personalized medicine, and discusses their clinical applicability in greater detail. We also consider critical challenges such as data privacy, algorithmic bias, and regulatory frameworks, offering insights into future directions for AI in hypertension management. This review aims to serve as a comprehensive reference for researchers and clinicians seeking to leverage AI-driven solutions for improving hypertension care.</p>