According to the United Nations 2020 report on the aging of the world population, approximately 727 million people were aged 65 or older, representing a substantial share of the global population. By 2050, the older adult population is projected to reach approximately 1.5 billion. The older adult population may be concerned about frequent clinical visits and the risk of changes in their health conditions. Wearable devices are well-suited for monitoring older adults because they display up-to-date health information and track health metrics longitudinally. For example, fall-detection devices are worn all day and can detect unexpected falls and dispatch warning notifications. Wearable medical devices can collect data on a range of health parameters in older adults, including heart rate, blood oxygen saturation, body temperature, physical activity, blood pressure, daily activity, and other metrics. Rapid growth in wearable technology devices is expected in the coming years, according to International Data Corporation (IDC), a market intelligence firm that has researched the wearables market. Various companies that provide services to users of these devices collect real-time data and store it in their databases. Additionally, researchers have employed various methods to collect data from older adults on wearable devices. It is a significant challenge to analyze such data in real time to identify risks, trends, and patterns and to provide informed, online, and automated feedback to the user so that they can improve their condition. In this chapter, we will use a dataset of older adults from the Health and Retirement Study in the USA. For these types of data analyses, we will present step-by-step scenarios and illustrate various machine learning algorithms to provide readers with a comprehensive understanding and enable them to apply their knowledge to solve real-life problems.