This chapter provides an overview of AI-driven smart healthcare monitoring systems that highlights the incorporation of emerging sensor-based technologies with sophisticated artificial intelligence algorithms that could enhance diagnostic precision, facilitate early disease identification, improve neonatal care, address geriatric problems, and personalize methods for treatment. Machine intelligent healthcare systems increasingly depend on multiple sensors, such as wearable, non-wearable, implantable, and ambient sensors that support real-time monitoring of physiological signals along with improving patient safety via real-time data collection. The combined use of these sensors with artificial intelligence algorithms, which consist of machine learning algorithms, deep learning, and large language models, has enabled sophisticated, reliable applications in support of clinical decision-making, such as early disease detection, medical imaging, chatbots, and digital health record administration. Furthermore, advanced methodologies such as transformer-based architectures and reinforcement learning have shown considerable progress in predictive analytics, progression of illness, and treatment planning, and hence, such contributions simplify healthcare services. Despite such advancements in the healthcare field based on various technologies, significant issues remained highly concerning, such as data confidentiality, privacy, openness, and equal access possibilities, that highlighted the gap and stressed the need for effective, reliable governing frameworks. Real-world applications based on multiple domains of the healthcare area that include smart disease detections, smart hospital administration, telesurgery, neonatal care, geriatrics care, drug discovery, and dosage monitoring may highlight both the transformative potential and inherent security risk of an artificial intelligence-driven healthcare system. Future paradigms such as hybrid AI models, blockchain-enabled safe data exchange, and quantum computing for high-dimensional medical data promise to revolutionize healthcare. The conclusion provides the insights to stress the need for interdisciplinary collaboration to maximize the potential of smart healthcare that could maintain reliability and efficiency while ensuring the safety and protection of the patient’s welfare.