Skin cancer represents a serious and growing global public health challenge, with incidence rates increasing steadily across diverse populations. Early diagnosis and timely intervention play a vital role in reducing mortality and improving treatment outcomes. Traditionally, accurate diagnosis has relied on the expertise of trained dermatologists, posing accessibility challenges in resource-limited settings. In recent years, artificial intelligence (AI) technologies particularly deep learning and advanced image processing techniques have emerged as promising tools for assisting in medical image analysis and automated disease detection. This study presents a computer-aided diagnosis (CAD) system based on deep convolutional neural networks (CNNs) designed for the early detection of skin cancer through dermoscopic image analysis. The CNN model was trained and tested on a curated dataset, and achieved a prediction accuracy of 90.5%. The system demonstrates strong potential for identifying malignant skin lesions with high precision, contributing to the rapid, non-invasive, and cost-effective assessment of skin abnormalities. The use of deep learning in this context not only improves diagnostic speed but also offers a scalable solution for screening large populations. These findings underscore the transformative role of AI in dermatological diagnostics and highlight the capability of CNN-based systems to complement clinical expertise. Future work will focus on enhancing model robustness, incorporating multi-modal data, and validating performance through real-world clinical trials.