Purpose of Review <p>Single photon emission computed tomography (SPECT) and positron emission tomography (PET) cardiac imaging have evolved, providing innumerable data points for the clinician reader to analyze to achieve accurate diagnosis and guide management. The advent of artificial intelligence (AI) could play a pivotal role in better harnessing these data and improving nuclear cardiology workflows. In this review, we explored the current applications of AI in various aspects of nuclear cardiology.</p> Recent Findings <p>Innovative studies have explored the use of AI, particularly deep learning models, to identify ideal patient candidates for stress-only imaging to reduce radiation exposure and acquisition time. Furthermore, there is published evidence that deep learning can provide efficient methods to achieve reliable image segmentation, attenuation correction, and image registration. In addition, AI-based disease diagnosis and risk prediction models have been shown to perform similarly if not better than expert readers in some settings. Beyond coronary artery disease, there are promising results of deep learning algorithms to improve diagnostic imaging for cardiac sarcoidosis and amyloidosis.</p> Summary <p>Recent advancements in AI models provide an opportunity to refine the nuclear cardiology workflow ranging from patient selection to disease prediction and reporting. Promising results from these early studies need to be replicated in larger heterogenous patient populations to demonstrate generalizability prior to widespread adoption in clinical practice.</p>