Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis although it is highly manual and labor-intensive. Due to this issue, many are interested in comput-er-aided diagnostics technologies to assist pathologists in their diagnostic. Specifically, deep learning (DL) has become a viable remedy in this field. Nonetheless, the capacity of existing DL models to extract comprehensive visual features for accurate classification is limited. Towards the end, this study proposes using ensemble models that combine the strengths of multiple transformers and deep learning model architectures. By leveraging the collective knowledge of these models, the ensemble enhances classification perfor-mance and enables more precise and effective kidney cancer detection. This study compares the performance of these suggested models to previous studies, all of which used the publicly accessible Dartmouth Kidney Cancer Histology Dataset. This study showed that the Vision Transformers, with an average accuracy of over 99%, were able to achieve high detection accuracy across all complete slide picture patches. In particular, the CAiT, DeiT, ViT, and Swin models outperformed ResNet. All things considered, the vision Transformers consistently produced an average accuracy of 98.51% across all five folds 5 folds. These results demonstrated that Vision Transformers might perform well and successfully identify important features from smaller patches. Through utilizing histo-pathological images, our findings will assist pathologists in diagnosing kidney cancer, resulting in early detection and increased patient survival rates