Nail disorders encompass a wide spectrum of conditions that range from benign infections to indicators of systemic diseases and malignancies. Accurate and timely diagnosis is crucial but often hindered by limited access to specialists and the complexity of visual assessment. In recent years, artificial intelligence (AI) has emerged as a transformative tool in dermatology, with growing applications in the diagnosis and management of nail diseases. AI-powered image analysis, particularly through deep learning models, offers promising accuracy in identifying common nail conditions. This chapter examines the current landscape of AI integration in the diagnosis of nail disorders, with a particular focus on onychomycosis and nail psoriasis. Convolutional neural networks (CNNs), among other deep learning models, are being trained using clinical, dermoscopic, and even smartphone-acquired images, mirroring the diagnostic modalities most commonly employed by dermatologists. In onychomycosis, AI models have demonstrated diagnostic performance approaching that of experienced clinicians. For nail psoriasis, AI is primarily utilized for disease classification among other nail conditions and in the semi-quantitative assessment of severity using tools like the Nail Psoriasis Severity Index (NAPSI). While current results are promising, further improvements in AI performance depend on the availability of larger, high-quality, and diverse datasets, along with continued oversight and validation by expert clinicians.