Nowadays, hate speech has started to spread rapidly with the increasing use of social media. Such abusive discourse can cause reputation damage and adversely affect psychological health. Large social media companies are trying to prevent this situation and increase their service quality with the increasing number of users every day. In this context, our study proposes a system that detects hate speech in texts and warns the user against hate speech. The project was implemented using machine learning, deep learning and language modeling techniques with a labeled hate speech dataset collected from various sources. The results show that BERTweet and DistilBERT language models achieved 90% accuracy. On the other hand, although the success of the classical models was lower, they were more effective temporally.