In this study, we address the shared task of classifying violence-inciting texts from YouTube comments related to violent incidents in the Bengal region. We seamlessly integrated domain adaptation techniques by meticulously fine-tuning pre-existing Masked Language Models on a diverse array of informal texts. We employed a multifaceted approach, leveraging Transfer Learning, Stacking, and Ensemble techniques to enhance our model’s performance. Our integrated system, amalgamating the refined BanglaBERT model through MLM and our Weighted Ensemble approach, showcased superior efficacy, achieving macro F1 scores of 71% and 72%, respectively, while the MLM approach secured the 18th position among participants. This underscores the robustness and precision of our proposed paradigm in the nuanced detection and categorization of violent narratives within digital realms.