Skin cancer is among the most prevalent and dangerous cancers globally, making early detection crucial for improving patient outcomes. Traditional diagnostic methods are often slow and subjective, prompting the need for automated solutions using Artificial Intelligence (AI) and deep learning (DL). This study explores the application of deep learning models, such as Convolutional Neural Networks (CNNs), transfer learning, and ensemble techniques, for skin cancer detection, and classification utilizing datasets such as HAM10000, ISIC, and PH2. Key models, including LesNet, SkinNet-16, and hybrid approaches, demonstrated high accuracy in diagnosing melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Transfer learning with models such as VGG16 and ResNet improved results even when the amount of data available was small. However, challenges like data imbalance, generalizability, and clinical validation persist, emphasizing the need for broader datasets and real-world testing. Despite these challenges, deep learning in skin cancer is a promising breakthrough in the years to come where it will provide precise, effective, and affordable methods of detecting skin cancer at its earliest stages.