Skin cancer, particularly melanoma, poses significant health risks globally. Early detection is crucial for effective treatment and improved patient outcomes. Dermoscopy, a non-invasive imaging technique, has enhanced dermatologists' ability to examine skin lesions. Recent advancements in artificial intelligence (AI), especially deep learning, have shown promising results in automating the analysis of dermoscopic images for skin cancer detection. AI models, particularly convolutional neural networks (CNNs), have been trained on large datasets of dermoscopic images, achieving diagnostic accuracies comparable to or surpassing those of experienced dermatologists. These AI systems can assist in identifying malignant lesions, thereby aiding in early diagnosis and reducing the workload on healthcare professionals. However, challenges remain, including the need for diverse and representative datasets, addressing biases in AI models, and ensuring the clinical applicability of these technologies. This paper reviews the current state of AI applications in dermoscopic image analysis for skin cancer detection, discusses the methodologies employed, evaluates the performance of various AI models, and examines the potential impact on clinical practice. The integration of AI into dermatology holds the promise of enhancing diagnostic accuracy, improving patient outcomes, and optimizing healthcare resources.