The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in healthcare is emerging as a transformative approach to address the growing challenges posed by an aging global population and the increasing prevalence of chronic diseases. This paper explores the potential of AI and IoT to enhance real-time health monitoring and deliver personalized care solutions. Current healthcare systems often struggle with issues such as data accuracy, latency, and patient engagement, leading to suboptimal outcomes. Through a comprehensive review and the development of a detailed integration model, this study assesses the impact of AI-IoT systems on improving data accuracy and timeliness in health monitoring. Furthermore, it identifies the key challenges related to data security and privacy in these systems and proposes effective solutions. The research focuses on chronic disease management, particularly for conditions such as diabetes and hypertension, and considers diverse patient demographics to ensure broad applicability. By leveraging advanced AI technologies, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, alongside a variety of IoT devices like wearable sensors, this study aims to pave the way for more personalized, efficient, and responsive healthcare delivery.