Skin cancer is a major global health concern, where early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic methods, such as manual visual inspection and conventional machine learning models, often suffer from subjectivity, high computational costs, and limited annotated data. Althoug deep learning has improved automated skin cancer detection, existing models face challenges like overfitting, insufficient generalization, and complex architectures that limit real-time clinical application. To address these limitations, we propose MAF-DermNet, a deep learning framework that integrates Multi-Scale Attention Fusion (MAF) with depthwise separable convolutions for efficient and accurate skin cancer detection. Our approach enhances data diversity using DCGAN-based synthetic augmentation to improve model robustness. By leveraging multi-resolution inputs and a residual attention block, MAF-DermNet effectively captures subtle lesion features while preserving critical low-level information. Extensive experiments demonstrate exceptional performance, with accuracy exceeding 99.9% and macro F1 scores above 99.5%. In addition to its superior classification capabilities, MAF-DermNet offers enhanced interpretability and computational efficiency, making it well-suited for clinical deployment. Future work will focus on integrating clinical metadata and optimizing the model for diverse healthcare settings to further improve early diagnosis and treatment outcomes.