Social networking sites have increased in popularity in recent years because they provide users with a platform where they can connect worldwide and discuss their interests. However, people will benefit from a good environment thanks to the social network. Random cyberbullying is wild, and it poses a serious threat to the physical and mental health of the victim. It is important to find the right protection to detect and prevent this. The proposed method aims to detect the presence of cyberbullying activities on social networks such as Twitter where the tweets are scrapped and made it into a clean dataset and preprocessed the data by removing the “stopwords,” “lemmatization,” “tokenization” and more which is explained further. The dataset is then compared using the “NLPNatural Language Processing (NLP) models” (e.g., ‘stanzaStanza’, ‘RobertaRoBERTa’, ‘NLTK’). In this article, we also compare the performance of three different NLPNatural Language Processing (NLP) tools in detecting cyberbullying on Twitter profiles. We evaluated the tool using data from recorded tweets. We found that NLTKNatural Language Toolkit (‘NLTK’) outperforms “StanzaStanza” and “RoBERTaRoBERTa” in detecting cyberbullying. “NLTK” achieved an accuracy of 94%, while “StanzaStanza” and “RoBERTaRoBERTa” together achieved an accuracy of 19%.