Being able to continuously/remotely collect patient data has been the major motivation for developing wearable devices and mobile applications. These devices enable real-time monitoring of vital signs, physical activity, and other health metrics, providing valuable data for early detection of health issues, personalized treatment plans, and improved patient outcomes. The collected data is then fed into the second level of IoT-DS-AI nexus, where data cleaning, processing and analytics take place to provide a comprehensive insight into the bulky data. Various types of analysis and visualizations are offered to assist the decision-makers. Interactive dashboards, apps and other interfaces are provided for quick review of the health trends for individual patients and populations. Finally, AI comes into the picture with a focus on predicting the health trends. Since patient state is continuously monitored, a better and accurate prediction about future risk is possible. Hence, achieving SDG 3 becomes possible by having more frequent data points and more precise predictions about health risks. This chapter sheds lights into techniques from all three domains of IoT, DS and AI that offer huge potential to improve the patient outcomes in a quick and cost-effective manner as compared to the conventional healthcare systems.