Social media bullying is a rising issue, and it can have serious and lasting effects. It's crucial to identify social media cyberbullying to stop harm and advance a secure and encouraging online community. In this paper, we give an overall information of the different approaches and techniques that are currently being used to detect online bullying. We discuss the benefits and drawbacks of the current detection techniques and point out potential directions for further study. The paper covers a variety of detection techniques, including hybrid, machine learning(ML), natural language processing(NLP), social-network analysis, and keyword-based approaches. We also take into account several contextual variables, such as cultural and societal variations, privacy issues, and ethical considerations, which can have an impact on the detection of cyberbullying. Overall, this evaluation offers a thorough overview of the present status of social media cyberbullying detection and suggests important areas for further study and improvement.