Recent advancements in computer vision, machine learning, and deep learning have stimulated anintensifiedconcern for developing effective approaches for the early detection and treatment of dermatological illnesses involving skin lesions. This review paper aims to provide a comprehensive overview of state-of-the-art techniques for detecting, segmenting, and classifying skin lesions, which are crucial for timely intervention and improved patient outcomes. By examining into the challenges associated with physical inspection, the review underscores the importance of leveraging automated methods for skin lesion analysis in healthcare settings.The primary objective of this review is to accurately identify and classify various types of skin lesions, utilizing a range of image formats such as dermoscopic and macroscopic images. By critically examining recent research articles focused on skin lesion classification, the survey puts light on the several methods employed in various publications, with a particular importance on the role of deep learning techniques. Deep learning, a subset of machine learning, has emerged as a powerful tool in this domain due to its proficiency to automatically learn hierarchical representations from data, leading to improved implementation in complicated tasks such as image classification.This survey highlights the benefits and drawbacks of different machine learning and deep learning approaches for skin lesion classification. By integrating and assessing the latest research findings, it aims to provide visions into the current state of the subject and identify areas for further improvement. The integration of machine learning and deep learning techniques in dermatology holds immense capability for enhancing diagnostic accuracy, facilitating early detection, and ultimately improving patient care. Therefore, this survey acts as a valuable resource for researchers, clinicians, and healthcare professionals seeking to use cutting-edge technologies for skin lesion analysis and diagnosis.