Objective ·To explore the value of synthetic magnetic resonance imaging (SyMRI) technology in the diagnosis of cervical lymph node metastasis in patients with oral cancer. Methods ·A retrospective analysis was conducted on patients admitted to Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine, from November 2023 to April 2024, who were pathologically diagnosed with oral cancer and had a clear lymph node metastasis status. Pre-operative maxillofacial magnetic resonance images of these patients were collected. Histogram features were extracted and screened from the quantitative maps generated by SyMRI, including synthetic T1, T2, and proton density (PD) maps, apparent diffusion coefficient (ADC) maps, and volumes of interest (VOIs) from contrast-enhanced images. The optimal range was determined by comparing the histogram parameters of different peritumoral regions. On this basis, habitat analysis was further carried out by combining the quantitative maps and ADC maps, and the habitat features of the tumor invasive sub-regions were extracted to construct a prediction model. The performance of the model was comprehensively evaluated using receiver operator characteristic curves (ROC curves), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results ·A total of 61 patients with oral cancer were included in the study. The area under the curve (AUC) value for predicting cervical lymph node metastasis based on the intratumoral histogram features extracted from SyMRI was 0.798 (95%CI 0.673‒0.924). Adding the ADC map could increase the AUC value to 0.818 (95%CI 0.635‒0.861). By combining the histogram features from the 12-mm peritumoral region with habitat features, the AUC value for predicting lymph node metastasis could be further increased to 0.907 (95%CI 0.812‒0.993). The analysis results of NRI, IDI, and DCA all showed that the predictive performance of the model was better than that of clinical diagnosis. Conclusion ·Based on SyMRI, combining the histogram features from the intratumoral and peritumoral regions with habitat features shows high performance in the prediction of lymph node metastasis in oral cancer, providing a feasible, contrast agent-free approach for rapid imaging and prediction of metastatic lymph nodes.