In the past few decades, melanoma incidence has risen sharply, emerging as a major challenge in public health. Consequently, the cornerstones of successful melanoma treatment remain early detection and accurate staging. Advances in artificial intelligence (AI) with deep learning—especially convolutional neural networks (CNNs)—have given the ability to foster tools that can analyze dermoscopic images, clinical metadata, whole-slide pathology scans, reflectance confocal microscopy (RCM), and even radiologic studies. At the beginning, AI was framed as a competitor to clinicians. However, nowadays AI has emerged as a diagnostic partner with many dermatologists—especially the less experienced ones—benefitting from AI-assisted workflows. In this chapter, a comprehensive review of AI in melanoma will be presented, including the integration of AI in dermoscopic analysis, smartphone apps, RCM, histopathology, as well as AI-assisted staging, treatment, and prognosis. Furthermore, the pitfalls, the limitations and the future path towards the clinical integration of AI in melanoma care and management will also be explored. The aim of this chapter is to serve as a foundational reference for clinicians, researchers, and developers who shape the future of AI-driven melanoma care.