Internet usage has become increasingly important in people’s lives without a doubt. But it has certain dark sides such as: Larceny of privacy, cyberstalking, phishing scams, online harassment, cyberbullying, and illegal works. Basically, cyberbullying is a burning issue that leaves a profound imprint on the victim. In recent years, there has been an instant growth in using machine learning algorithms to detect cyberbullying. This paper presents a comparative study of various classification-based machine learning and deep learning models for detecting cyberbullying. It proposes a multimodal approach that leverages four types of social content—text, images, video, and audio (TIVA)—to identify instances of cyberbullying. We investigated this problem by gathering a variety of datasets, which include various sources of data relating to other media platforms. The data contain text, audio as well as visual content and are labeled as bullying or not. The features considered are facial expressions with emotions, speaker recognition with the frequency of the voice, body language, and context with text and symbols. Then combined the classification techniques according to the task and feature used for comparison are made among their performances. All the statistics are done in Python Colab notebook. The resulting multi-format cyberbullying detection platform provides a comprehensive approach to detecting incidents such as cyberbullying. Author’s framework will outperform several state-of-the-art models by conducting experimental analysis on the multifile format.