Optical Coherence Tomography (OCT) provides high-resolution images of skin tissue structure and pathological features. Automated image analysis methods (such as segmentation and classification) are important for assisting skin disease diagnosis and treatment evaluation. These methods provide quantitative support for medical decisions. Compared with traditional methods and early machine learning (ML) techniques, deep learning (DL) improved analysis efficiency and reproducibility. It also reduced manual processing time significantly. This paper systematically reviewed the application progress of DL in skin OCT image analysis. It focused on technical approaches for image denoising, skin layer segmentation, and skin cancer diagnosis. The study identified key challenges including model generalization and data heterogeneity. The findings provide theoretical references and technical guidance for future research directions.