<p>Cyberbullying has garnered growing attention, yet existing research lacks nuanced insights into the dynamics of students’ cyberbullying profiles and the associated risk factors across multiple domains. This study aims to (1) investigate K-12 students’ cyberbullying profiles, (2) develop an AI predictive model for cyberbullying roles, and (3) examine the relationship between cyberbullying profiles and risk factors across four domains: information and communication technology (ICT) profiles, moral development, normative social influence, and demographic characteristics. Latent profile analysis of 4721 students identified three distinct cyberbullying profiles: “Cyber bully-victims”, “Cyber passive and defenders”, and “Cyber victims and bystanders”. Through AI-based model competition experiments, we identified 13 key predictors and constructed a robust Random Forest model that accurately predicts profiles. The results reveal that information dissemination literacy emerges as the most prominent predictor, and students with higher literacy tend to fall into “Cyber bully-victims” or “Cyber victims and bystanders”. Additionally, students with excessively high moral emotions or difficulties in making moral judgments about harm are more prone to be “cyber victims and bystanders”. These findings reinforce a deeper understanding of cyberbullying profiles and their antecedents among K-12 students, and offer researchers an AI-based methodological approach for robust prediction of students’ cyberbullying in practice.</p>