Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for generating synthetic nuclear medicine images, aimed at improving the interpretability and utility of nuclear medicine protocols. We discuss advanced image generation algorithms designed to recover details from low-dose scans, uncover information hidden by specific radiopharmaceutical properties, and enhance the sensing of physiological processes. By analyzing 30 of the newest publications in this field, we explain how deep learning models produce synthetic nuclear medicine images that closely resemble their real counterparts, significantly enhancing diagnostic accuracy when images are acquired at lower doses than the clinical policies' standard. The implementation of deep learning models facilitates the combination of NMI with various imaging modalities, thereby broadening the clinical applications of nuclear medicine. In summary, our review underscores the significant potential of deep learning in NMI, indicating that synthetic image generation may be essential for addressing the existing limitations of NMI and improving patient outcomes.