Machine learning has significantly transfigured healthcare by providing innovative solutions across numerous domains, with its impact on knee osteoarthritis being particularly notable. In healthcare, ML facilitates early disease detection, personalized treatment strategies, and outcome prediction, all of which are critical for effective patient care. Specifically in Knee Osteoarthritis, Machine learning techniques have significantly advanced diagnostic capabilities by analyzing patient data such as demographics, symptoms, and imaging results to predict disease progression and grade patients based on severity. This study examines how well machine learning algorithms work with X-ray images to assess the degree of osteoarthritis in the knee. By effectively predicting severity levels using attributes taken from X-ray pictures, machine learning (ML) presents a possible option. Various ML algorithms, including convolutional neural networks and ensemble methods, are explored for their ability to analyze X-ray images and extract relevant patterns indicative of OA severity. Challenges such as data preprocessing, feature extraction, and model interpretation are addressed, aiming to enhance the reliability and clinical utility of ML-based severity assessment models for knee OA. © 2025 Elsevier B.V., All rights reserved.