With the exponential increase of technologies and the growth of social media users, bullying takes different methods to reach its targets. Cyberbullying has been emerging lately in the form of bullying through voice memos, videos, and most frequently in the form of text messages. Bullies might use the rich and expansive environment that social networks offer to target their victims with their attacks. Cyberbullying has an especially negative impact on younger generations, who value social affirmation above everything. Studies have found a clear link between cyberbullying and suicidal ideation, particularly in teens. This concerning trend needs the development of efficient ways for identifying and combating cyberbullying, thereby protecting young lives. Many techniques can be used to identify the bullies’ linguistic patterns and create a detection model that will automatically identify instances of cyberbullying and whether it can lead to the happening of suicide or not. This project proposes a hybrid model of BiLSTM and EmoBERTa for detecting cyberbullying and checking the possibilities of it leading to the happenings of suicide. The dataset was run on different models and the proposed model yields the best average performance. By putting such detecting mechanisms in place, we can make the Internet safer. Early detection of cyberbullying enables intervention, which protects vulnerable people and may avoid disasters.