An unchecked proliferation of malignant cells may spread throughout the body, causing cancer. Skin cancer is one of the worst types of the disease since it may spread quickly and leave no survivors. Deoxyribonucleic acid (DNA) breaks in skin cells produce mutations, which in turn lead to cancer. In the early stages, skin cancer tends to spread slowly to other areas of the body, thus early detection is critical. The biopsy technique is well recognized by doctors when they are identifying illnesses. The skin is scrapped or detached in the biopsy process, and specific laboratory experiments can be conducted on these skin samples. This process is time-intensive and often frustrating because of this. Computer-aided screening allows early-stage diagnosis of skin cancer. Typically, macroscopic photographs are branded as quantifiable images that are generally used for computer processing, and these images are captured utilizing a typical digital camera and video. Medical photographs have many problems, such as low lighting and the appearance of artifacts such as skin lines, highlights, repetitions and hair in the pictures. Researching skin lesions is very difficult because of these complications. Several measures are involved in detecting skin cancer at the computational level, such as preprocessing, recognizing trends, selecting features, and extraction of features and identification. The area of computer vision, in particular, has benefited greatly from deep learning.'' Due to deep learning's ability to automatically learn and extract meaningful features from raw data, it removes the requirement for feature engineering altogether. Deep learning has become a potent feature learning technique because to recent advancements in software and hardware technology. Engineering features by hand is a laborious and time-consuming procedure that necessitates the use of human expertise. A overview of deep learning methods for skin cancer early detection is presented in this article. Deep learning techniques are examined.