Background: Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally, yet clinical guidelines remain unclear on surgical recommendations for patients with mild forms of DCM. This is in part due to limitations in current MR imaging interpretation and complex mechanisms of neurological deterioration. Supervised machine learning (ML) models can help to identify clinical and imaging indicators of deterioration within mild DCM patients. Methods: 127 MRI scans (T2w, Diffusion Tensor Imaging, and Magnetization transfer scans) accompanied by a series of clinical tests underwent a semi-automated analysis to derive quantitative metrics. Random forest classifier, Support Vector Machine, and Logistic Regression models were trained and tested to predict 6-month neurological deterioration within patients. Results: The ML models performed, on average, better than previous studies with a balanced accuracy ranging between 70-75%. “Advanced” imaging metrics such as diffusion tensor imaging and magnetization transfer scans played an important role in improving model accuracy but only when used near the maximally compressed disc level, suggesting that limited yet targetted imaging metrics support ML model performance. Conclusions: The inclusion of specific, targeted imaging and clinical metrics support ML model performance in predicting neurological deterioration within mild DCM patients.