Machine learning has become increasingly important in medical diagnosis, yet its effectiveness depends on access to large, reliable, and high-quality datasets. During epidemics and emerging diseases, such as COVID-19, acquiring sufficient real-world medical images rapidly is challenging. To address these issues, this study presents an Adaptive Conditional Generative Adversarial Network (Adaptive-CGAN) integrated with a cloud-based medical image processing framework. The proposed approach makes three main contributions. First, Adaptive-CGAN generates high-fidelity synthetic medical images that closely resemble real samples while improving the distinction between real and fake images. Second, a scalable TensorFlow Records (TFRecords)-based pipeline is implemented on Google Cloud Platform (GCP) to support efficient storage, loading, and processing of large-scale medical datasets. Third, a real-world COVID-19 medical image dataset comprising four disease classes is compiled and used to enhance diagnostic prediction. Experimental evaluation was conducted against several baseline generative models, including AC-GAN, WGAN, Pix2Pix, BigGAN, and CWGAN, with AC-GAN serving as the primary like-for-like baseline. Adaptive-CGAN improved classification accuracy from 93.75
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