Hence, there is a crucial need for MUL studies that Machine unlearning (MUL) is an arising field in machine specifically target ViT models. In response to this need, we learning that seeks to erase the learned information conduct comprehensive machine unlearning experiments on of specific training data points from a trained model. Despite ViT models using the recently proposed MUL algorithms the recent active research in MUL within computer and datasets [5]. Specifically, we utilize two most widelyused vision, the majority of work has focused on ResNet-based ViT models, ViT-base and ViT-large, applying and analyzing models. Given that Vision Transformers (ViT) have become recent machine unlearning algorithms on these architectures.