Cancer continues to be one of the world’s foremost causes of mortality. While skin cancer is less common compared to other types, it still poses a significant challenge due to the difficulty in early detection of its symptoms and signs. This study focuses on harnessing machine learning and deep learning models to enhance skin cancer detection through image analysis. Several models were assessed and contrasted, including Random Forests, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Networks (CNN), and Logistic Regression (LR). CNN emerged as the core model, integrated with other classification methods to enhance accuracy. Particularly, the integration of CNN with LR has notably increased accuracy, providing a more efficient approach for quickly diagnosing skin cancer through image analysis.