Oral cancer is increasingly common and poses significant dangers, with early detection being crucial for effective treatment. However, due to patient privacy concerns, there is limited access to large datasets. To address this challenge, we explore using advanced transfer-learning techniques for the early detection and classification of oral squamous cell carcinoma (OSCC) in histopathological images. Specifically, we implemented various transfer learning methods, including EfficientNet, ResNet, NASNet, and DenseNet, to classify these images. Among the models tested, EfficientNet achieved the highest accuracy of 97.85%, demonstrating its effectiveness for early oral cancer detection in small datasets. The promising results of transfer learning in raising the diagnostic accuracy even when data is scarce. Further development of these models with integration into clinical practice would help in early diagnosis and better prognosis for patient outcome.