Cyberbullying is defined as deliberate behavior by an individual or group using electronic means of communication, such as transmitting a message or publishing criticism about a victim on social media in order to harm or slander the victim. Cyberbullying via social media may occur at any time and from any location, as opposed to conventional bullying, which often happens during face-to-face conversation. Currently teenagers have their identity from their social media accounts and are fluently affected if any negative response is seen on their accounts. This research aims to mitigate cyberbullying. They often feel discouraged, disheartened, and hurt when subjected to aggressive responses to their posts or harsh, targeted communications. In this work, we’ve probed various algorithms to describe cyberbullying and compare them to get the best result. Experimenters have extensively used SVM and CNN for the discovery of cyberbullying on various social platforms. We explore the use of text feature extraction methods—Count Vectorizer (CV) and Term Frequency-Inverse Document Frequency (TF-IDF)—combined with various machine learning algorithms for real-time detection of cyberbullying. By assigning importance to words beyond their raw frequency, TF-IDF demonstrated superior performance across most algorithms compared to CV. Algorithms like Linear SVC and Random Forest performed exceptionally well. This approach can be instrumental in developing efficient, real-time cyberbullying detection systems, particularly on social media platforms.