Pneumonia has recently reported the highest number of deaths in the entire world. Consequently, diagnostic procedures ought to be precise. Conversely, the problem of class imbalance is a universal issue in medical image classification that may result in biased models that do not perform well in underrepresented classes. The issue of class disproportion is successfully addressed in the work under consideration, as a cGAN generates high-quality synthetic images of the minor classes, allowing for the creation of a balanced dataset that, in turn, leads to high sensitivity of the model and consequently improves its overall performance. This paper presents a new Conditional Generative Adversarial Network (cGAN) architecture to improve the detection of pneumonia in the provided chest X-rays. To mitigate the issue of data imbalance in the dataset, this work suggests a conditional GAN-based augmentation process for synthetic X-ray images by producing clinically viable and label-coherent synthetic X-ray images. The framework also includes validation achieved through the application of either SSIM/FID or balanced training, which improves the accuracy of pneumonia detection and leads to diagnostic conclusions unlike those of existing methods. Current approaches are less successful compared to this study, as the system achieves 96.5% accuracy, 95.2% sensitivity, and 96.1% specificity, with a high F1 score. The suggested framework has a good performance scale, making it applicable in medical applications. This paper demonstrates the capacity of cGAN to develop pneumonia diagnosis machines that are feasible and user-friendly in healthcare institutions.