Background and objective: Wearable technology has become increasingly essential in managing cardiovascular disease (CVD), offering innovative solutions for real-time monitoring and personalized care. Artificial intelligence (AI) is playing a growing role in enhancing the capabilities of wearable devices, yet the global research trends and knowledge gaps in this area remain underexplored. This study aims to provide a comprehensive bibliometric analysis of wearable technology research for CVD management, with a specific focus on the integration and impact of AI. Methods: We conducted a bibliometric analysis of literature published between 2014 and 2024, sourced from major academic databases. The analysis employed citation, co-citation, and co-word mapping techniques using tools such as VOSviewer and Bibliometrix to identify key studies, emerging themes, and research gaps in wearable technology and AI for CVD management. Results: AI-powered wearables improve CVD diagnostics and patient outcomes, but challenges remain in clinical integration and data interoperability. These devices also play a crucial role in early atrial fibrillation (AF) detection, enhancing diagnostic accuracy and supporting timely medical interventions. AI-enhanced portable ECG technology further improves real-time decision-making in cardiovascular care, offering a transformative approach to personalized, evidence-based medicine. Conclusions: AI integration in wearable technology is revolutionizing CVD management, offering precise, personalized care. However, challenges such as data security, algorithmic bias, and clinical validation persist. Ensuring privacy requires strong encryption and regulatory compliance. Large-scale trials, standardized data frameworks, and clinician training are essential to accelerate adoption, ensuring AI-powered wearables are effective, equitable, and sustainable in healthcare. © 2025 Elsevier Ltd