This study aims to enhance oral cancer diagnosis using machine learning and digital image processing techniques, focusing on the potential of Convolutional Neural Networks in developing automated diagnostic tools. Early detection of oral cancer is very much important to improve the survival rates. Traditional diagnostic methods, including biopsies and visual examinations, are often time-consuming and subject to human error. By leveraging Convolutional Neural Networks, this research seeks to improve diagnostic accuracy, reduce subjectivity, and enable early detection through automated image analysis. The study evaluates different Convolutional Neural Networks architectures, comparing their performance with traditional machine learning models and previously established deep learning techniques. It examines key factors such as feature extraction, classification accuracy, and computational efficiency to determine the most effective model. The ultimate goal is to develop a robust, AI-driven system capable of assisting healthcare professionals in identifying oral cancer with greater precision and reliability.