Acne is one of the most prevalent skin disorders globally, especially affecting teenagers and young adults, often resulting in emotional stress and permanent scarring. Although computer-assisted diagnostic tools have been studied, most current approaches lack detection at the individual lesion level and are too computationally intensive for mobile use. This paper introduces an efficient, real-time acne detection system leveraging YOLOv8n, optimized for mobile and edge deployment. The model is trained on a specially labeled dataset with four acne lesion types—papules, pustules, nodules, and comedones—annotated at the lesion level. To overcome data imbalance and scarcity, we utilized image augmentation and per-class replication methods. Hyperparameter optimization was automated using Optuna to further boost model accuracy. The system’s performance was assessed using standard object detection benchmarks, including Precision, Recall, mAP@0.50 (0.501), and mAP@0.50:0.95 (0.162). Results confirm the framework’s capability to deliver accurate lesion-level identification with fast execution speed, supporting its practical use in mobile dermatology applications.