Cyberbullying poses a significant threat to online safety, particularly impacting younger users who are more susceptible to toxic digital interactions. This research presents an innovative AI-based solution designed to automatically detect harmful behavior and toxic comments in online environments. By leveraging advanced deep learning models, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) networks combined with Natural Language Processing (NLP) techniques, the system is trained on extensive datasets of user comments to identify both subtle and overt signs of cyberbullying. The approach incorporates effective preprocessing methods such as TextVectorization, along with caching, shuffling, and batching techniques to optimize performance. The AI system’s ability to process and classify harmful content in real-time makes it a powerful tool for monitoring online communities, promoting safer interactions, and significantly reducing incidents of online harassment. By equipping digital platforms with this technology, the solution aims to create healthier online environments, making it an essential resource for organizations committed to enhancing user safety.