The cervical spine a common site of injury in the vertebral column, with severe injuries resulting in permanent disabilities. One of the most common neck injuries is whiplash, and the plethora of clinical symptoms and sequelae have been classified as whiplash-associated disorders (WAD). The risk of sustaining WAD has been shown to be significantly influenced by gender. Females are at higher risk of developing symptoms. Additionally, finite element human body models have proven to be fundamental tools for better understanding injury mechanics. As such, the aim of this work is to create a new finite element of the female cervical spine that will more accurately represent the group most affected by such injuries. The geometry of the model was obtained from the CT scans of a 49-year-old female subject and was generated using a hybrid methodology of combining medical images and parametric studies. The vertebrae, the intervertebral discs (IVDs), the facet joints, and the ligaments were modelled, with most assumed to be isotropic materials with linear elastic properties, with the exception of the IVDs, which were modelled as hyperelastic material properties with the Mooney-Rivlin definition. Additionally, all ligaments were set to work only in tension. Due to the high element and node count in the complete model, it was necessary to partition the model into functional spinal units (FSUs), each comprising pairs of vertebrae. The FSUs were subjected to six moments of pure moments of 1Nm working in flexion, extension, axial rotation, and lateral bending. Throughout the applications of the loads, the range of motion (ROM) was monitored. The accuracy of the developed model was validated by comparing output predictions with the data found in the literature. Most of the obtained results fell within the standard deviation of the corresponding test averages. However, there was a noticeable difference between male and female neck ROM. For future model validations, it is imperative to incorporate female-specific data to enhance the accuracy of future model validations wherever feasible.