In this paper, the detection of Skin Cancer using Artificial Intelligence & Machine Learning Concepts is presented. Convolutional Neural Networks (CNN) in the area of image recognition are one of the many complex challenges that Deep Learning has gained attention for in recent years. Transfer Learning (TL) approaches are also becoming more and more common as a way to retrain some of the layers of Neural Networks that learnt from a generic dataset, putting it to use in another situation. This is because Neural Networks can occasionally attain more than 50 layers deep. In this work, we try to use CNNs and TL methods on pre-trained Neural Networks to separate seborrheic keratosis and nevus benign tumours from melanoma skin cancer. Skin cancer can be detected early thanks to AI. Deep convolutional neural networks, for instance, can be used to construct a system that assesses skin image data to detect skin cancer. For skin cancer to be effectively treated and to have better outcomes, early detection is crucial. The use of artificial intelligence (AI) technologies, such as shallow and deep machine learning-based techniques that are trained to detect and categorise skin cancer using computer algorithms and deep neural networks, is therefore being made to assist in the diagnosis of skin cancer. The purpose of this study was to categorise and list the many AIbased technologies that are used to find and classify skin cancer. By examining the relationship between the size of the data set, the number of diagnostic classes, and the performance measures employed to assess the models, the study also looked at the validity of the papers that were chosen. The results shows the effectiveness of the method put in this paper