Skin cancer has become a dangerous condition in the modern world. It can be classified as either non-melanoma or melanoma (benign or malignant). Scars, dark spots, or changes in the skin appearance can all be indicators of skin cancer. Changes in irritability, size, form, or color could be indicators of skin cancer. This project concentrates on ensembling the extracted features from the vgg16 and MobileNet pre-trained models and fed to the ML models and a deep neural network. To enhance some current procedures and create modern approaches that would enable precise models that decrease the time gap between the diagnosis and treatment period in identifying skin cancer. With ensemble features from vgg16 and mobileNet pre-trained models, experimental findings show the effectiveness of the deep neural network and achieve an accuracy of 87%.