This study presents an advanced system for detecting skin cancer using Convolutional Neural Networks (CNNs), enhanced by Batch Normalization to improve model stability during training. CNNs, widely recognized for their effectiveness in image analysis, form the foundation of this system, which is designed to address the global challenge of skin cancer detection. The model’s capacity to manage a variety of datasets, providing enhanced adaptability, is one of its primary characteristics. It tackles the common issue of imbalanced skin cancer data by employing techniques such as SMOTE, undersampling, and oversampling, resulting in increased accuracy and sensitivity, particularly for less common cases. Comparative experiments demonstrate that this model surpasses previous benchmarks in identifying skin disorders. The integration of Group Normalization further boosts stability, and the combined methods for addressing data imbalances enhance the model’s ability to generalize across varied data. This makes the system a highly valuable tool for healthcare professionals. Experimental evaluation on the HAM10000 dataset achieved a test accuracy of 96.4%, a training accuracy of 99.74%, and a validation accuracy of 96.35%, with a minimal loss of 0.0079. The adaptive data balancing strategy further enhanced classification, improving F1-scores by 12–15% for rare classes such as melanoma and dermatofibroma, while preserving 98.2% accuracy for majority classes. The study underscores the potential of modern deep learning techniques to transform the interpretation of medical images, setting a new standard to combating skin diseases in healthcare.