The efficacy of vision-based deep learning models in malware classification is frequently hindered by data scarcity and severe class imbalance. To address this critical challenge, this paper proposes the implementation of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) to synthesize high-fidelity, two-dimensional malware representations. We conditioned the network on five distinct classes: benign, spyware, trojan, virus, and worm. Designed to generate 224x224 RGB images, the model was trained on a structured subset of malware datasets. Empirical results over 60 training epochs demonstrate highly stable convergence and the effective elimination of mode collapse, a common flaw in standard GANs. Following the robust training phase, the model successfully generated a completely balanced dataset comprising 10,000 synthetic images (2,000 samples per class). This reliable data augmentation strategy provides a vital foundation for mitigating class imbalance, thereby improving the predictive accuracy and generalization capability of downstream deep learning-based malware detection systems.