The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.