This chapter explores the use of the Internet of Medical Things (IoMT) to support remote diagnosis and monitoring in the biomedical domain, highlighting its ability to facilitate real-time analysis of clinical data. Although the DATALOG project, presented as a case study, is focused on the development of the implementation of data mining to identify patterns and generate early warnings, the developed infrastructure and architecture are designed to integrate future Machine Learning (ML) implementations. Initially, Machine Learning techniques and methods that could be applied in this context are addressed, such as the prediction of adverse clinical events and the automatic classification of medical data, exploring their potential in preventive diagnosis and home hospitalization. Finally, the system architecture, monitored variables, and experimental results are described, showing how IoMT and data mining can improve the accuracy of the monitoring and contribute to a preventive and proactive care model.