This study aims to enhance the accuracy and efficiency of oral cancer diagnosis through the application of deep learning techniques in medical image analysis. The research employs convolutional neural networks (CNNs), specifically ResNet and DenseNet architectures, for the classification of oral cancer images into malignant and benign categories. Data preprocessing involves resizing, normalization, and augmentation to optimize model performance. Evaluation metrics including accuracy, loss, specificity, and sensitivity demonstrate varying performance across different CNN models. DenseNet architectures consistently outperform ResNet and conventional CNNs in terms of accuracy and sensitivity metrics. The results showed that DenseNet consistently outperformed ResNet, achieving higher accuracy and sensitivity, which are crucial for early cancer detection. The findings underscore the transformative potential of deep learning in augmenting clinical decision-making for oral cancer detection. Integration of these advanced technologies into healthcare workflows could significantly improve early detection rates and treatment outcomes, paving the way for personalized medicine approaches in oncology.