With the advancement in the healthcare and medicine sector, now a day’s average life span of humans has increased. Due to an increase in average life expectancy, the demographic of old age people has increased. According to a World Health Organization report, old age people have more chances to get with fall and recurrent fall (World Health Organization in Who global report on falls prevention in older age, 2007). For elder people, human falls may create severe medical issues and injuries too. Because of the ever-growing old age people, there is an urgent requirement for the development of fall detection systems. Fortunately, with the help of advanced biomedical wireless sensor networks, the internet of things, Microelectromechanical sensors, and human–computer interaction it is possible to address this issue of human fall detection. In this research article, we have presented a survey on human fall detection methods and Systems. Human fall detection can be developed using one of the following ways: vision-based techniques, ambient sensor-based techniques, and wearable device-based techniques. In this review article, we have presented a brief review of the above-mentioned methods. Various machine learning methods for fall detection and activity of daily life have been discussed rigorously in this article with available literature.