Skin disorders significantly impact quality of life, necessitating advanced diagnostic tools. Machine Learning (ML) and Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), offer promising solutions. ML aids in image segmentation and lesion classification, while DL captures intricate features for improved accuracy. Challenges include lim ited annotated data, the ‘black box’ nature of AI decision-making, and seamless integration into clinical workflows. Recent advancements such as development of ML and DL algorithms and availability of publicly available datasets, hold promise for earlier diagnoses and improved outcomes in dermatology. Building ML and DL models robust to variations in image quality, lighting conditions, and patient demographics is hence, paramount to enhance the accuracy and efficiency of skin image analysis in clinical practice. In conclusion, while ML and DL techniques show promise, exploring hybrid approaches combining the strengths of both could lead to more robust diagnostic tools for dermatology, revolutionizing skin disorder diagnosis and treatment.