Interleukin-6 (IL-6) is a critical cytokine involved in immune regulation, inflammation, and the pathogenesis of various diseases, including autoimmune disorders, cancer, and the cytokine storm associated with severe COVID-19. Identifying IL-6 inducing epitopes, the short peptide fragments that trigger IL-6 production, is crucial for developing epitope-based vaccines and immunotherapies. However, traditional methods for epitope prediction often lack accuracy and efficiency. This study presents RAG_MCNNIL6, a novel deep learning framework that integrates Retrieval-augmented generation (RAG) with multiwindow convolutional neural networks (MCNNs) for accurate and rapid prediction of IL-6 inducing epitopes. RAG_MCNNIL6 leverages ProtTrans, a state-of-the-art pretrained protein language model, to generate rich embedding representations of peptide sequences. By incorporating a RAG-based similarity retrieval and embedding augmentation strategy, RAG_MCNNIL6 effectively captures both local and global sequence patterns relevant for IL-6 induction, significantly improving prediction performance compared to existing methods. We demonstrate the superior performance of RAG_MCNNIL6 on benchmark data sets, highlighting its potential for advancing research and therapeutic development for IL-6-mediated diseases.