ABSTRACT: Aside from people with significant neurological abnormalities, it's unclear whether conservative or surgical therapy of spinal disorders is better for each patient. This research looks into the use of Artificial Intelligence (AI) and Machine Learning (ML) in the therapy of spinal diseases decision-making. A supervised ML was trained and tested using the datasets of 70 patients with goal of accurately predicting the Oswestry Disability Index (ODI) 5 months following operation or the beginning of conservative therapy. In addition, using tenfold cross-validation, created an approach that forecasts the ODI of 5 arbitrarily chosen testing patients. The use of AI in this study also enabled for a comparison of the genuine patient data after 5 months with different treatment forecast, revealing variances of up to 19.6%. In the supervised version used here, ML can detect patients who'd receive from conservative treatment earlier on and, on the other hand, avoid unnecessary delays and painful for patients who'd gain from surgical treatment. Furthermore, despite tiny diagnostic categories, this strategy can be applied in various other fields of medical a