This chapter examines the role of artificial intelligence (AI) in diagnosing, treating, and assessing acne vulgaris, emphasizing the transformative impact of AI models, particularly deep learning and convolutional neural networks (CNNs), on dermatology. AI is enhancing diagnostic accuracy, personalizing treatment plans, and facilitating real-time monitoring of acne progression. Most research on acne and AI models focuses on classification, with limited studies mainly comparing acne with rosacea. Various techniques have been used for acne assessment, including support vector learning, label distribution learning, object detection with You Only Look Once models, and CNN combinations like AcneGrader and Acne-RegNet. The input data varied, with cases evaluated using high- and low-quality images, en face or 3D images of face, and different acne severity classifications such as Investigator’s Global Assessment and Global Acne Grading. Mobile images have also been used for training models, achieving high accuracy. Future developments may involve integrating Internet of Things technology with CNNs and cloud computing to enhance acne care in healthcare settings. In terms of treatment, AI systems used for assessment could also assist in follow-up care, while AI-powered virtual tools may be more useful for adolescents who are more comfortable with digital tools. Chatbots may provide valuable information to patients, though certain cases will still require expert monitoring.