With the rapid proliferation of electronic devices, the volume and sophistication of malware have surged, posing critical cybersecurity threats. Traditional malware detection approaches face challenges such as limited generalization, unbalanced datasets, and high computational costs. To address these issues, this study introduces the LLM-Powered Transformer Framework for Multi-Class Malware Detection, an image-based approach integrating Large Language Models (LLMs) and transformer architectures with Convolutional Neural Networks (CNNs). The proposed framework enhances malware classification by leveraging data visualization, balanced sampling, and data augmentation techniques, achieving over 98.86% accuracy across four open-source datasets. Furthermore, this study makes two key contributions: first, it provides granular insights into malware classification performance using confusion matrix analysis, aiding cybersecurity professionals in refining detection strategies. Second, the balanced sampling approach eliminates the need for additional datasets, minimizes hardware overhead, and dynamically adjusts sampling weights for optimal learning. Additionally, data augmentation techniques mitigate overfitting, enhancing the model's adaptability to diverse malware variants. Comparative analysis with state-of-the-art methods demonstrates the proposed framework's efficiency in achieving high accuracy while maintaining computational feasibility. These advancements establish a robust foundation for real-world malware detection and cybersecurity applications.