Artificial intelligence AI) has transformed various aspects of dermatology. While it was initially used to evaluate pigmented lesions, its use has expanded to non-melanoma skin cancers (NMSC), particularly basal cell carcinoma and cutaneous squamous cell carcinoma. AI technology is currently integrated into multiple stages of standard clinical care. In the diagnostic phase, machine learning algorithms can assist with lesion classification (benign vs malignant), provide differential diagnoses, and enable the accurate lesion identification through clinical and dermoscopic image analysis, as well as new noninvasive imaging modalities. This can be of great value for patients with limited access to healthcare specialists. AI has also been employed in histopathological analysis in tumor recognition, margin assessment, and tumor grading. During follow-up, it helps monitor for new or recurrent tumors, enabling early intervention whenever necessary. Lastly, AI can assist with treatment planning, particularly in complex surgical cases, while prognostic models can stratify patients based on recurrence risk or tumor behavior. However, key limitations remain, particularly the lack of clinical context, such as patient history, skin phototype, and anatomical site of lesion, which can lead to false positives and reduced specificity. Continued development and cautious integration are essential for AI to fully realize its potential in NMSC care.