| We investigate the revolutionary potential of combining artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) to revolutionise healthcare. By seamlessly integrating these cutting-edge technologies, we enable healthcare professionals and stakeholders to make data-driven decisions, optimise resource utilisation, improve patient outcomes, and reduce environmental systemic inefficiencies. This breakthrough not only overcomes the issues of integrating AI, ML, and IoT in healthcare, but it also adds to sustainable and efficient medical practices, indicating a brighter and more resilient future for the healthcare sector. Artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) have all had a significant impact on healthcare due to the progressive integration of technology. The early use of computers in healthcare was for administrative duties, but by the late 1950s and 1960s, researchers were exploring more complex applications, such as medical diagnostics. The introduction of Electronic Health Records (EHRs) in the late 1960s and 1980s paved the way for enhanced data analysis in healthcare. Early AI and expert systems, such as MYCIN, were among the first to incorporate AI into clinical decision-making. The growth of the internet and health information technology in the 1990s and 2000s expedited the integration of AI, machine learning, and IoT. AI, machine learning, and IoT are now integrated into numerous parts of healthcare, with the goal of personalising care, improving results, and increasing efficiency. AI and ML are poised to revolutionise healthcare in a variety of ways. They will increase diagnostic accuracy, predict disease outbreaks, and control healthcare expenses. Wearable technology and remote monitoring will be combined, allowing for more rapid treatments and improved chronic condition management. AI will improve robotic surgery, simplify administrative tasks, and speed up drug discovery. Telemedicine and virtual health services will grow, particularly in underdeveloped areas. However, ethical, privacy, and regulatory considerations will be critical. Healthcare personnel will require new abilities to effectively use AI and ML technologies. AI can also tackle global health issues including disease surveillance and health inequalities. The future of AI and machine learning in healthcare is about integrating new technologies in an ethical, patient-centered, and internationally inclusive manner, with the goal of improving healthcare quality, accessibility, and effectiveness. An AI-enabled Internet of Things (IoT) system for real-time health diagnostics and personalised treatment suggestions combines innovative technologies to transform healthcare. By merging IoT-enabled wearable devices with AI algorithms, the system continuously monitors important health factors such as heart rate, blood pressure, and body temperature. Real-time data is securely uploaded to cloud platforms for processing, allowing the system to spot anomalies and diagnose any health risks right away. Using machine learning algorithms, the system generates personalised therapy suggestions based on individual health profiles, increasing the accuracy and efficacy of medical procedures. This technology provides healthcare providers with meaningful insights and enables remote patient monitoring, which reduces hospital visits and promotes preventative treatment. By improving accessibility and enabling rapid medical treatment, the AI-IoT framework alters traditional healthcare procedures, resulting in better patient outcomes and paving the path for a data-driven, patient-centric healthcare ecosystem. In this idea, patient data is analysed using an IoT sensor to improve patient and hospital details. Medical services are increasingly moving towards e-clinical and drug-assistive apps. Medical care administration advancement is most popular in each country. AI-enabled IoT can provide high-quality medical care management while also serving innovative and progressive possibilities. AI used in medical care administration would increase the odds of developing cutting-edge illness forecasts and proposing preventive methods and diagnostics. Distributed computing would support registration, correspondence, storage, and a wide range of information handling in the medical care framework. To stream healthcare data, the IoT requires an information storing and handling system. The body sensor and RFID labels are used to collect various human physiological data, which is subsequently shared via remote innovation to be stored and managed in the cloud, and served through IoT applications for better information utilisation. The clinical information shown here incorporates edge registration to provide only the most critical information to the appropriate customer. The medical data handled here incorporates edge computing, which delivers only the essential data to the relevant user.