Drug-drug interactions (DDIs) pose significant challenges in modern healthcare, often leading to adverse effects and treatment inefficiencies. Extracting DDIs from biomedical literature is crucial for understanding drug synergies and potential risks. This research addresses the need for improved DDI extraction by proposing a deep multimodal fusion approach. The study’s scope encompasses integrating various modalities to enhance DDI extraction accuracy. Methodology: Our methodology employs a deep multimodal fusion model that combines information from diverse modalities to enrich the representation of biomedical text. Specifically, we leverage the structure of chemical graphs, chemical structure images, chemical formula, and textual descriptions of drugs from DrugBank to augment traditional text-based models. Through this integration, our model gains a comprehensive understanding of the relationships between drugs, enabling more accurate DDI extraction. Results: Evaluation of our proposed approach demonstrates significant improvements in DDI extraction performance compared to baseline methods. By leveraging multimodal information, our model achieves higher precision, recall, and F1-score in identifying DDIs from DrugBank documents and Medline abstracts. Furthermore, our approach exhibits robustness across different datasets, indicating its generalizability and effectiveness in real-world scenarios. Conclusion and Discussion: This research underscores the potential of deep multimodal fusion techniques in enhancing DDI extraction from biomedical literature. By integrating diverse sources of information, our approach improves the accuracy and reliability of DDI identification, thereby facilitating better decision-making in clinical practice and drug development. Additionally, the proposed methodology can be applied to other biomedical tasks requiring multimodal information fusion.