With the help of the Internet of Things, the wearable and smart device industries are making big strides in healthcare, products like hearing aids that work with Bluetooth and robotic caregivers. In recent years, there has been a dramatic shift from a centralized care delivery system based mostly on hospitals toward a more decentralized healthcare system that puts the patient first. Healthcare has undergone a rapid transformation as a result of several technological advances. Innovative healthcare services and apps now use 4G and other connectivity technologies. As the healthcare sector expands, more and more applications are likely to generate vast quantities of data of varying shapes and sizes. Currently, the Internet of Things (IoT) and Artificial Intelligence (AI) are causing a revolution in the healthcare business regarding detecting, diagnosing, and treating several diseases. Artificial intelligence increasingly realizes the revolutionary character of IoT technologies, which promote innovation in the creation of linked medical equipment because of their widespread presence in the sector, from smart phones to robots. The Internet of Medical Things (IoMT) refers to the interconnectivity of devices used in healthcare. The practice of using internet-enabled medical devices like smart watches, fitness trackers, and various sensors to collect and share health data. IoMT aims to enhance healthcare quality by facilitating real-time monitoring of a patient's health state to enhance the effectiveness and efficiency of treatment. In addition to facilitating better patient outcomes and industry-wide research and development, IoMT allows for collecting and analyzing massive volumes of data. The goal of the IoMT and Machine Learning (ML) in smart healthcare is to expand access to healthcare and enhance senior citizens' quality of life and efficiency of healthcare delivery. The IoMT enables continuous tracking of a senior citizen's health thanks to a network of connected wearable’s, sensors, and other medical devices. In this way, machine learning algorithms can monitor patients' vitals and accurately predict the onset of severe health problems. Health care practitioners may be more proactive in their responses and provide more prompt medical intervention, leading to better patient outcomes and lower healthcare costs. While long short-term memory (LSTM) excels at time-series data like heart rate and blood pressure values, which are often gathered in geriatric care, the author presents a hybrid method that combines LSTM with Random Forest. Random Forest and Boosted Decision Trees rely on many decision trees to conclude. It is possible to categorize patients based on their health condition and identify potential dangers.

Internet of Medical Things, Monitoring Patients, LSTM, Random Forest.