Skin cancer is one of the most prevalent and rapidly increasing forms of cancer worldwide, making early detection essential for improving patient survival and treatment outcomes. Traditional diagnostic methods rely heavily on visual examination and dermoscopic analysis by dermatologists, which may sometimes be subjective and dependent on clinical expertise. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for automated skin cancer detection and classification. These techniques utilize medical image datasets, particularly dermoscopic images, to identify patterns and features associated with malignant and benign skin lesions. This review presents a comprehensive analysis of recent research on ML and DL-based approaches for automated skin cancer detection. Various algorithms such as Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), and transfer learning models are examined in terms of their methodologies, datasets, and performance metrics. Additionally, this study highlights the advantages, limitations, and challenges associated with these techniques. The review also discusses future research directions, including the development of more diverse datasets, interpretable models, and integration of AI-based systems into clinical practice to enhance diagnostic accuracy and healthcare efficiency.