The diagnostic evaluation of skin lesions has been transformed by recent advances in artificial intelligence. The two main AI paradigms—rule-based expert systems and data-driven machine learning approaches, with a focus on convolutional neural networks and their use in dermoscopic image processing are thoroughly examined in this research. These AI systems can achieve diagnostic accuracies that are on par with or better than those of skilled dermatologists, according to empirical studies. This is especially true when it comes to the segmentation, classification, and risk stratification of skin lesions, which allows for more accurate and timely clinical interventions. Despite these encouraging outcomes, a number of significant obstacles need to be overcome before AI can be completely incorporated into standard clinical practice. Widespread adoption is nevertheless hampered by problems including intrinsic biases in training datasets, the opacity of intricate model architectures, and challenges integrating AI tools with current healthcare practices. Standardizing imaging procedures and curating a variety of high-quality datasets are crucial for ensuring stable performance and clinical dependability. Future studies should also look into creating more sophisticated AI frameworks that can get over the present restrictions on data privacy and model generalizability, such as multimodal, vision-language, and federated learning models. A strong dedication to ethical integration and continuous external validation are essential to maximizing AI’s potential to improve melanoma detection and overall patient care. The FDA has approved several AI-based techniques for melanoma detection, demonstrating the growing popularity of AI-driven skin cancer diagnostics in the United States. Widespread clinical usage is still hampered by regulatory issues and the requirement for representative, diversified datasets.