This article delineates the possible role of AI in imaging adipose tissue to improve visceral adipose tissue (VAT) and ectopic fat segmentation. As this was a review article, researchers addressed crucial questions about AI and its prediction capabilities, providing an overview of how it may compare with traditional imaging techniques. AI-powered programs may offer more comprehensive segmentation of adipose tissue with deeper insights into its composition and function. Notably, Convolutional Neural Networks, a specialized kind of deep neural network, are advantageous for detecting spatial hierarchies in images. This allows researchers to more accurately delineate various layers of adipose tissue. Moreover, Deep Learning models such as these can use imaging data for identification of complex features or relevant biomarkers used to predict clinical outcomes, monitor treatment response, and overall personalize patient care. With AI’s ability to integrate vast amounts of information, the study of VAT and its relationship with cardiovascular risk factors will advance significantly. This information can include imaging modalities, clinical records, genetic predispositions, lifestyle factors, longitudinal health data, and metabolic markers. Still, there are many limitations in using artificial intelligence in this way, including the lack of transparency in explaining its decision making process in a “black box” manner. In all, it has yet to meet safety and efficacy standards and requires a lot more learning and training in order to be approved by the FDA and similar organizations.