This research introduces a groundbreaking strategy that significantly improves the accuracy of speech signal analysis in predicting cyberbullying, drawing inspiration from Aquila-hunting tactics. Mel-frequency cepstral coefficients and chroma features are initially obtained from the voice dataset. It effectively selects the most essential MFCC features, ensuring both relevance and low dimensionality. The algorithm closely mimics the Aquila's superior hunting method—soaring (searching), scanning (evaluating), and swooping (acting)—considering each feature pairing as either ‘prey’ or a ‘solution.’ The effectiveness of each solution is rigorously assessed using a long short-term memory (LSTM) classifier, achieving an impressive accuracy of 93.5% with the selected features, compared to just 85% when using all MFCC and chroma features. Additionally, this innovative method results in a noteworthy 10.50% reduction in computation time. In conclusion, this study highlights the essential need for creative strategies in the early detection and swift resolution of cyberbullying, empowering individuals to navigate society with dignity.