Skin exposed to the sun is more likely to have skin cancer, which is an unnatural growth of skin cells. However, skin that isn’t frequently exposed to sunlight might potentially develop this prevalent type of cancer. This study attempts to detect and categorize six different types of skin cancer using clinical images accurately. The proposed approach used both clinical images and Metadata to feed into a hybrid model that benefits from deep learning in feature extraction and machine learning in classification. Many experiments have been applied to the PAD-UFES-20 dataset. Seven distinct algorithms of machine learning are applied with ten different pre-trained deep learning models. The hybrid model consists of MobileNetV2 as a feature extractor and logistic regression as a classifier for images, and random forest as a classi-fier for metadata has proved its superiority. The accuracy was 95.6%, precision 96.8%, recall 95.6%, F1-Score 95.7%. The evaluation process shows that metadata has an effective role in improving accuracy compared to previous studies that depend on images only in classification.