The integration of Artificial Intelligence (AI) into dermatology represents a paradigm shift, poised to address critical challenges in diagnostic accuracy, access to care, and clinical workflow efficiency. This review synthesizes recent breakthroughs focusing on the evolution from unimodal image analysis to sophisticated multimodal systems. We detail the technical foundations of deep learning models, including Convolutional Neural Networks (CNNs) and Transformers, and critically evaluate their performance in skin oncology, inflammatory diseases, and aesthetic dermatology. Despite promising results, often rivaling or surpassing clinician performance, significant hurdles related to data bias, model interpretability, and regulatory integration persist. We conclude by outlining future directions, emphasizing the need for diverse datasets, explainable AI, and robust clinical validation to fully realize AI’s potential as a synergistic tool in patient-centered dermatologic care.