This study explores the application of transfer learning models in oral cancer detection using RGB images. By applying background removal, resizing, and filtering techniques, the effectiveness of ResNet50, MobileNetV3Small, and VGG19 models has been explored. Results reveal that among the three models, MobileNetV3Small achieves 97.78% accuracy with fast processing, while ResNet50 maintains consistent performance with 97.8% accuracy across training and testing. VGG19 outperforms the other models, reaching 99.24% accuracy, albeit with longer processing time. Although limited by a dataset of 128 images, this study emphasizes VGG19’s potential for highly accurate oral cancer identification without adverse societal or environmental impacts. Future research will address dataset limitations and explore application development to further enhance the model’s robustness.