Atopic dermatitis (AD) is a chronic, inflammatory skin condition characterized by substantial clinical heterogeneity, posing significant challenges to clinicians in diagnosis, severity stratification, and management. Artificial intelligence (AI) has emerged as a transformative tool in medicine and dermatology, offering innovative solutions for disease screening, severity grading, and personalized therapeutic optimization. In AD, machine learning models have been utilized to identify novel biomarkers for therapeutic development, leading to more effective, safer, and AD-specific therapies. Additionally, these models have demonstrated the ability to diagnose AD and differentiate it from other dermatologic conditions, reducing reliance on subjective clinical assessments. Future integration of AI tools into clinical practice, such as leveraging real-time transcriptomic and proteomic data to predict optimal therapeutics, monitor treatment responses, and develop AI-embedded wearable technology for remote and continuous disease monitoring, can rapidly transform AD management. However, as technology advances, ensuring bias reduction through representative training data sets and establishing proper regulatory oversight to protect patient safety and privacy will be critical for its successful and widespread adoption. As AI continues to revolutionize AD management, its integration into clinical practice holds the potential to improve diagnostic accuracy, enhance personalized treatment approaches, and bridge health care disparities, ultimately improving human health.