Particularly in assessing osteoarthritis using KL (Kellgren-Lawrence) grading, recent in-depth convolutional neural networks (CNNs) have greatly improved the categorisation of knee X-rays. Deep learning is used in CNN approaches to automatically extract features from X-ray images, therefore enabling enhanced accuracy in osteoarthritis severity determination. Modern models such ResNet and DenseNet have been used on vast annotated knee X-ray datasets with great accuracy in separating the KL grades. These systems give radiologists quick assessments, therefore enabling prompt interventions. Remedies from Ayurveda remain quintessential in taking hold of knee osteoarthritis from the root, though there are modern technologies developed. Emphasis on such Ayurvedic Treatment, healing through herbs, shrubs remedies, food and nutrition modifications, and physiotherapies, aiming to alleviate pain and enhance joint function. Prior research indicates that the Ayurvedic formulations including Ashwagandha and Guggulu, can reduce inflammation and improve patients’ mobility whilst bearing osteoarthritis. Deep learning CNN with Ayurvedic treatments presents a significant interdisciplinary approach, while precise imaging and classification tools guide personalized Ayurvedic therapies. Integration of such contemporary methods may lead to optimum treatment plans that take care of both immediate symptoms and prior causes of such health conditions, finally increasing positive patient outcomes in knee joint recovery.