Nowadays, image synthesis has become essential in the medical field for lever- aging deep learning technique to improve decision- making. Our proposed research work combines Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to synthesize medical im- ages, enhancing diagnostics, medical training, and image analysis. The model presented combines a Discriminator, and a Variational Autoencoder to capitalize on the strengths of both VAEs and GANs. The Decoder is tasked with generating synthetic medical images, the Discriminator evaluates their distinguishing factor, and the VAE learns a probabilistic mapping from input to latent space, ensuring a structured representation of underlying medical features. The training process involves a decoder creating realistic medical images, a discriminator distinguishing real from synthetic ones, and a VAE capturing meaningful data variations in the latent space. Verified on the dataset sourced from the Kaggle. The model refines its parameters iteratively using a training loop, resulting in enhanced quality and variety of generated medical images. The proposed VAE- GAN model demonstrates its efficacy by generating diverse and realistic medical images. The structured latent space contributes to interpretability, making the images suitable for purposes like data augmentation, anomaly detection, and machine learning model training.