Skin lesion segmentation is an important step in the diagnosis and treatment of skin cancers, especially melanoma. This paper investigates the use of machine learning (ML) and convolutional neural networks (CNNs) to accurately and efficiently segment skin lesions in dermoscopic images. We assess the performance of several models, describe the process of implementing CNN-based segmentation, and highlight recent advances in the field. Our findings suggest that CNNs outperform conventional approaches in terms of segmentation efficiency and accuracy, particularly when sophisticated designs and training methods are used. When compared to traditional techniques, the results show that CNNs significantly improve segmentation performance. This suggests a possible avenue for incorporating cutting-edge AI technology into clinical dermatology in order to improve patient outcomes and diagnostic accuracy.