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Abstract: Cyber bullying has become a growing concern in today's society, with more and more people turning to the internet to harass and intimidate others. Digital forensics is an essential tool for investigating cyber bullying activities, as it allows for the collection and analysis of digital evidence. However, traditional digital forensics techniques can be time-consuming and require a significant amount of human effort. In this paper, we propose the use of machine learning algorithms to aid in the investigation of cyber bullying activities. By training these algorithms on a dataset of known cyber bullying incidents, we can create a predictive model that can automatically classify new instances of cyber bullying. This can significantly reduce the time and effort required for investigations, allowing for a more efficient response to cyber bullying incidents. The challenges associated with using machine learning for cyber bullying detection, including the need for high-quality training data and the potential for bias in the algorithms. We also explore the various types of digital evidence that can be used in cyber bullying investigations, such as social media posts, emails, and instant messages. We present a case study in which we apply our proposed approach to a real-world cyber bullying incident. Our results show that the machine learning algorithm was able to accurately identify the cyber bullying activity with a high level of precision, demonstrating the potential of this approach for improving the efficiency and effectiveness of cyber bullying investigations.