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% people above 60 are facing problem of Cervical Spondylosis (CS) commonly it’s a deteriorate condition of cervical spine. Even it is asymptomatic, cause’s pain at 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 proposing effective remedies that lessen the complexity indicated above, together with a proportionate analysis of ML with the effects of cervical spondylosis on working personnel and important considerations for medical implications. This research framed with two objectives: Assessment of existing research methodology, CS medical consequences, research advantages and its limitations; the integration of medical parameters versus CS consequences for medical diagnosis. It integrated the proportional analysis by comparing different categories such as age groups, job types, symptom severity to understand proportions and statistical significance. The considered key factors and patters which helps to uncovering the main contributing elements like as ergonomics, stress, physical inactivity using ML-driven feature importance or model interpretability techniques. It also identified and analyzed the at-risk employee groups using ML which helps to predict which employee demographics and job roles are most susceptible to CS.