We developed a novel method to generate 3D isotropic super-resolution prostate MR images using a class of machine learning algorithms known as Generative Adversarial Networks (GANs). We use GANs to generate super-resolution images with 3D SVR image slices as inputs. Super-resolution is enforced as the discriminator network is trained to distinguish the output image from in-plane T2 FSE images, resulting in the generation of super-resolution images. We use unpaired GANs since slices of 3D SVR do not usually have corresponding super-resolution images. The result is a generated continuous 3D volume with super-resolution throughout all three planes in isotropic voxel size.