Well, as, all of us know, there has been a phenomenal increase in the number of people using social media. As much as there are so many benefits of social media, there is a disadvantage of it too. Some of the social media platforms are been used for harassment and bullying in different ways. May it be tweets, comments, Text, images or videos. Hence a lot of platforms end up relying on hate speech detection. This ensures that such obscene acts are dealt with by combating these users in order to bring them to a halt. But hate speech is subjective. While some may get upset or offended others can take it as a joke and something that is not wrong in any way and vice versa. Hence there are times that it becomes challenging to find clues of such offensive actions and respect feelings and sentiments of the majority of the population. In this case Machine Learning techniques come in handy. The principal objective of the present work is to discuss several hate speech detection methods which have been mainly conducted employing different computational ML and DL approaches. It has been revealed that hate speech detection is, in fact, mostly a classification model. There are two subcategories in the classification and they include the Hate Speech as well as the Non Hate Speech. There are Multiple techniques and algorithms which we are going to explore in this research of ours.