Personalized Remote patient monitoring in the intensive care unit (ICU) is a critical observation and assessment duty required for precision medicine. We recently developed a cloud-based intelligent. We use a remote patient monitoring (IRPM) architecture that is state-of-the-art in risk classification. The most accurate prediction is made using machine learning, but with few characteristics that rely on vital indicators. Physiological characteristics gathered within and outside of hospitals are widely employed. We made a big contribution in this effort. enhance the basic IRPM framework's capability by developing three machine learning models for Measurements of readmission, abnormalities, and next-day vital signs We give a formally represented version of a feature engineering algorithm, as well as the construction and validation of three replicable machine learning algorithms ICU patient readmission, anomaly, and next-day vitals prediction models.