Magnetic Resonance Imaging (MRI) is increasingly used to localize prostate cancer, but the subtle features of cancer vs. normal tissue renders the interpretation of MRI challenging. Computational approaches have been proposed to detect prostate cancer, yet variation in intensity distribution across different scanners, and even on the same scanner, poses significant challenges to image analysis via computational tools, such as deep learning. In this study, we developed a conditional generative adversarial network (GAN) to normalize intensity distributions on prostate MRI. We used three methods to evaluate our GAN-normalization. First, we qualitatively compared the intensity of GAN-normalized images to the intensity distributions of statistically normalized images. Second, we visually examined the GAN-normalized images to ensure the appearance of the prostate and other structures were preserved. Finally, we quantitatively evaluated the performance of deep learning holistically nested edge detection (HED) networks to identify prostate cancer on MRI when using raw, statistically normalized, and GAN-normalized images. We found the detection network trained on GAN-normalized images achieved similar accuracy and area under the curve (AUC) scores when compared to the detection networks trained on raw and statistically normalized images. Conditional GANs may hence be an effective tool for normalizing intensity distribution on MRI and can be utilized to train downstream deep learning tasks.