Advanced medical image processing techniques have significantly contributed to the computer aided detection (CAD) and classification of medical images. This has helped the clinicians for devising effective treatment plans and improved patient outcomes. Medical research heavily relies on extensive datasets for discovering rare diseases and advancing diagnosis methods. However, privacy concerns often limit access to sensitive medical data, hindering research progress. Synthetic data emerges as a solution to this challenge, offering privacy-preserving alternatives for generating diverse datasets. In this paper, we explore the application of generative adversarial networks (GANs), including DCGAN, WGAN, and WGAN-GP, for synthesizing chest X-ray (CXR) images. Our study focuses on evaluating the performance of these GAN variants in terms of stability and image quality. Particularly, we highlight the effectiveness of the modified WGAN-GP with adaptive λ and AdaBelief optimizer, showcasing its potential for generating high-quality synthetic CXR images. Evaluation metrics such as Fréchet Inception Distance (FID) and Inception Score are employed to assess image quality, with results indicating the superiority of the modified WGAN-GP. Overall, our findings demonstrate the promise of synthetic data generation in medical imaging research, offering avenues for privacy-conscious dataset augmentation and clinical application.