This degenerative disease of the spine often manifests in an individual as chronic pain and limited mobility if diagnosed late. Traditional techniques of diagnosis are lengthy and sometimes do not possess the sensitivity required for early diagnosis. This paper discusses innovative solutions which have utilized Artificial Intelligence (AI) and Machine Learning (ML) techniques to promote the early detection of the disease-the spondylosis. Our proposed models are expected to detect slight patterns indicating the onset of early spondylosis by taking into account clinical data, medical imaging, and a history of patients. We apply various machine learning algorithms: support vector machines, decision trees, and neural networks, for the study efficiency of these methods in disease prediction. The techniques yield higher accuracy and efficiency than those used in traditional diagnosis. This study proposes the possible application of AI and ML for the diagnosis of spondylosis, thus opening up an opportunity for timely interventions and better patient outcomes.