Skin cancer, especially melanoma, is a global health problem around the world due to its high mortality rate. Traditionaldiagnostic methods such as visual inspections and biopsies are often limited by human error, time limits and dermatologist variations.Advances in artificial intelligence (AI) have led to increased opportunities to recognize skin cancer detection and diagnosis. Thisstudy examines how algorithms for AI in dermatology, particularly for machine learning, improve depth. Learning models and imageprocessing approach the accuracy and efficiency of skin cancer. A diagnostic system with an AI-operated diagnostic system cananalyze dermis images with accuracy and recognize malignant skin lesions with high sensitivity and specificity.The AI system includes methods to overcome image quality and environmental variability issues by using data records such asArchives International Skin Imaging Collaboration (ISIC) and enhance image improvements such as contrasting adaptive histogramcompensation (clahe) and Multi-Scale Retinex with Color Restoration (MSRCR). This paper analyses existing features, limitations,and future promises of AI in dermatology. Dermatologic including AI in practice can lead to skin cancer screening, making itaccessible, consistent, accurate, and lower mortality. This study aims to demonstrate how AI can diagnose AI before it is sprayedwith skin cancer, and to provide a valuable tool for both medical professionals and patients in the fight against this life-threateningdisease.Keywords: Skin Cancer, Melanoma detection, Deep learning, Dynamic thermal imaging, Skin Cancer Detection, Early Skin CancerDiagnosis, Melanoma Detection, Convolutional Neural Networks (CNN), feature fusion, infrared imaging, non-invasive screening,attention mechanisms