Tuberculosis continues to be a major global health concern, particularly in remote regions with limited access to healthcare. Early and precise diagnosis is essential to prevent its spread. This project utilizes high-performance computing (HPC) to tackle two major challenges: handling inconsistent medical data and enhancing TB detection. Due to the disproportion between healthy and TB-positive samples, Generative Adversarial Networks (GANs) are employed to create synthetic images, expanding the dataset and improving its diversity. This enriched dataset is then used to train Convolutional Neural Networks, which are highly effective in medical image processing. By leveraging HPC, we accelerate the CNN training process on large-scale, augmented datasets, significantly cutting down computation time while preserving accuracy. This approach enhances TB detection by integrating GAN-based data augmentation with CNN models, ensuring a quicker and more reliable diagnosis.