A black hole is an area in space with immense gravity that nothing (not even light) can escape from it. They are formed usually when a star dies. These black holes can be found by the detectors in space. They detect the gravitational waves by the ripples' effect. The isolated black holes that are invisible can be found using a method called gravitational lensing. Various black holes have been found using these methods. These black holes do not emit any light. This makes it difficult for scientists to observe the black holes directly. The effects of black holes on the nearby matter and light were observed by the scientists to detect a black hole. In this paper, a comprehensive review is presented about the techniques that can be used to detect and analyze black holes using machine learning. Information regarding the technique along with the conditions has been summarized in the paper. Other issues regarding the technology limitations, research challenges, and future trends are also discussed.