A medical problem with less severity causes minor health issues, while a more severe problem can pose a life-threatening risk to a person. It plays a role in how healthcare resources are allocated to different areas. In general, according to health exams, medical tests, and feedback from the person, the severity is labeled as mild, moderate, or severe. Generally, as age increases, 85% of people above sixty are facing the problem of cervical spondylosis (CS). Commonly, it’s a deteriorating condition of the cervical spine. Even if it is asymptomatic, it causes pain in the neck portion, stiffness, and neurological deficits in severe cases. These symptoms impact daily function, quality of life, and work productivity. The condition imposes a significant burden on healthcare systems due to its high prevalence and treatment demands. Early diagnosis and effective management are crucial to reducing disability and societal impact. This research proposes an analytical study for showing efficient solutions that help to reduce the above-mentioned complexities. This research is framed with three objectives: comparative analysis of existing advantages and its concern limitations in tabular form; analytical comparative working scenario of existing model based on the dataset and its supporting features and computational metrics; and identification of correlation among cervical spondylosis causing factors, metrics, and its supporting computational sub-metrics through quantitative features.