OBJECTIVE: To construct a nomogram model based on magnetic resonance imaging (MRI) radiomics combined with clinical characteristics and evaluate its role and value in predicting the prognosis of patients with cervical spinal cord injury (cSCI). METHODS: In this study, we assessed the prognosis of 168 cSCI patients using the American Spinal Injury Association (ASIA) scale and the Functional Independence Measure (FIM) scale. The study involved extracting radiomics features using both manually defined metrics and features derived through deep learning via transfer learning methods from MRI sequences, specifically T1-weighted and T2-weighted images (T1WI & T2WI). The feature selection was performed employing the least absolute shrinkage and selection operator (Lasso) regression across both radiomics and deep transfer learning datasets. Following this selection process, a deep learning radiomics signature was established. This signature, in conjunction with clinical data, was incorporated into a predictive model. The efficacy of the models was appraised using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) to assess their diagnostic performance. RESULTS: Comparing the effectiveness of the models by linking the AUC of each model, we chose the best-performance radiomics model with clinical model to create the final nomogram. Our analysis revealed that, in the testing cohort, the combined model achieved an AUC of 0.979 for the ASIA and 0.947 for the FIM. The training cohort showed more promising performance, with an AUC of 0.957 for ASIA and 1.000 for FIM. Furthermore, the calibration curve showed that the predicted probability of the nomogram was consistent with the actual incidence rate and the DCA curve validated its effectiveness as a prognostic tool in a clinical setting. CONCLUSION: We constructed a combined model that can be used to help predict the prognosis of cSCI patients with radiomics and clinical characteristics, and further provided guidance for clinical decision-making by generating a nomogram.