Using modern technology for managing and delivering care targeted specifically for the elderly is referred to as “digital care.” The accuracy and quality of healthcare are enhanced through such technologies, which gather patient data electronically and use it for streamlining the care process. The lack of health information systems and digital tools at all levels of healthcare, the sluggish adoption regarding manual medical records, the high cost of devices is just a few of the obstacles that must be overcome. The present study intends to enhance the fall detection system linked to chronic diseases. The suggested solution is designed with the use of machine learning (ML) algorithms, for recognizing particular conditions (such as walking, sitting, crawling, falling, etc.). For assessing and testing the suggested model, the research employed the datasets (sisfall), which were sourced from Kaggle. Furthermore, a variety of ML methods have been applied, including random forest (RF), decision trees (DTs), and KNN. For the DT algorithm, the computed accuracy in (sisfall) was 99.16%.