Abstract: Falls are a big health concern for the elderly who live in a vulnerable environment. Medical institutions havestudied falls extensively for more than two decades in order to limit their impact (e.g., loss of independence, fear of falling,etc.) and minimize their consequences (e.g. Cost of hospitalize- ton, etc.). However, the problem of elderly people falling doesnot only concern health professionals; it has also piqued the scientific community's curiosity. In reality, many scientificstudies have been conducted on falls, and several commercial products have been developed as a result of theseinvestigations. These studies have attempted to solve the problem by employing fall detection methods that include a varietyof sensing methods. Recently, researchers have moved their focus to fall prevention, with the goal of detecting falls beforethey occur. Early-fall prediction systems have begun to appear, notwithstanding their limitations to clinical investigations.Current assessments in this field, on the other hand, lack a common basis classification. The main contribution of this studyin this respect is to provide a comprehensive overview of senior falls and to suggest a generic classification of fall-relatedsystems based on sensor deployment. On the basis of this common ground classification, a comprehensive study schemeranging from fall detection to fall prevention systems has been carried out. Techniques for data processing in both the falldetection and fall prevention tracks are also discussed. The goal of this project is to give medical technologists working inthe field of public health a better understanding of fall-related systems.Keywords: Fall detection, Gait analysis, Machine learning, Activities of daily living, Elderly care, Health care systems