The aim of this article is to analyze the current overview of machine learning applied to cardiac imaging in nuclear medicine through a review of the recent literature. In recent years, new highly efficient artificial intelligence tools are revolutionizing the field of image analysis, being developed with the purpose of integrating the large volume of clinical and image information to improve the diagnosis of the disease and the risk estimate. The integration of artificial intelligence in daily clinical practice is being evaluated on several fronts and nuclear cardiology can benefit from the improvement in sensitivity, specificity, and diagnostic accuracy that the incorporation of these technologies can provide.