Knee arthritis is a significant concern affecting patient health, and early prediction is crucial for effective management. This project explores the use of deep learning models, including VGG16, ResNet50, DenseNet, and EfficientNetB5, for knee arthritis severity assessment. The most promising model, EfficientNetB5, was selected and further improved through image augmentation techniques, resulting in a remarkable accuracy improvement of 97%.