In today’s digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects individuals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based approach, primarily employing BERT and BiLSTM architectures, to effectively address cyberbullying. This approach is designed to analyse large volumes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five models, achieving an accuracy rate of 89.16%. This research is a significant contribution to “Computational Intelligence for Social Transformation”, promising a safer and more inclusive digital landscape.