Degenerative changes in the cervical spine can result in cervical spondylotic myelopathy (CSM), a condition that primarily affects middle-aged and older adults. It may lead to osteophyte formation, ligament hypertrophy, and intervertebral disc (IVD) herniation, reducing the spinal canal diameter and causing spinal cord compression that can result in neurological injury [1]. Although CSM affects approximately 1 in 50 adults, fewer than 20% of cases are formally diagnosed, largely because early clinical signs are subtle and nonspecific [2]. Surgical intervention is currently the only proven treatment, but it is typically reserved for patients with moderate-to-severe cases due to inherent procedural risks [3]. These challenges underscore the need for approaches that enable detailed biomechanical evaluation of the cervical spine across varying stages of pathology. In this study, finite element (FE) models were developed to investigate cervical spine mechanics in CSM. Two patient-specific models representing different severities of degeneration were reconstructed from medical images. Each model incorporated vertebrae, IVDs, facet joints, and ligamentous structures. Linear elastic material properties were assigned to bony and cartilaginous components, while hyperelastic constitutive models were used for discs and ligaments to better capture their nonlinear behaviour. The models were subjected to 1 Nm pure moments in flexion, extension, axial rotation, and lateral bending, and the resulting ranges of motion were quantified. Model validation was performed through comparison with published in vitro experimental data [4], and additional comparison with an established healthy cervical spine FE model [5] enabled evaluation of degeneration-related effects. Degenerated segments exhibited decreased mobility and elevated stresses in adjacent intervertebral discs. These preliminary results suggest increased spinal stiffness associated with degeneration, with higher disc stresses likely reflecting compensatory load redistribution. Despite limitations related to the use of healthy-subject data for validation, this work provides a foundation for developing computational tools to support improved clinical decision-making.