Skin cancer poses a significant threat to public health, with fatalities increasing due to insufficient awareness of symptoms and preventive measures. Early detection, especially of the most severe type, Melanoma, is pivotal for effective treatment. Leveraging deep learning techniques, our proposed system aims to autonomously provide the functionality by offering convenient image upload options, accurately predicting skin cancer types, educating users with facts about each type, and providing tailored guidance for the next steps, empowering users to make informed decisions regarding their skin health. Convolutional Neural Networks (CNNs) were chosen as the underlying deep learning methodology, renowned for their superior performance in visual imaging tasks. Developed in Python, utilizing TensorFlow and Keras, our CNN-based classification model was rigorously tested on the HAM10000 (Human against Machine) dataset. Evaluation metrics encompassed precision, recall, F1 score, ROC curve, and Confusion matrix. Our model achieved a noteworthy F1 score of 0.77% (weighted average), demonstrating its promise in accurate classification. Importantly, the Area Under the Curve (AUC) values for each cancer type came out to be, for akiec: 0.83, bcc: 0.83, bkl: 0.75, df: 0.94, mel: 0.93, nv: 0.99, vasc: 0.78 underscore its potential as a versatile tool for early skin cancer detection, with far-reaching implications for improving patient outcomes.