As cyberbullying escalates worldwide, the emotional toll on those targeted is profound, demanding attention. This question allows the framing of the detection of cyberbullying as a categorization puzzle, leveraging a spectrum of detection strategies from classical machine learning to advanced deep learning techniques via Natural Language Processing (NLP). It zeroes down to that capability which characterizes RNNs in parsing sequential data and interpreting the contextual nuances. This hence underscores their effectiveness and accuracy in the flagged detection of cyberbullying content. Directly compared to classical algorithms, it shows the best performance of RNN regarding accuracy, speed, and universality over different languages. The overall result of this research does, therefore, affirm the very strong promise and effectiveness of RNN frameworks toward the discrimination of cyberbullying across the various linguistic online environments, setting a firm ground for further development in cybersecurity.