Abstract: The onset of small intestinal polyps is insidious, with clinical symptoms that lack specificity, making detection challenging. Small bowel endoscopy is the most commonly used technique for examining small bowel diseases. However, this technique is cumbersome and has certain observational blind spots, such as behind the cecum, behind the intestinal valve. Therefore, employing non-invasive imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), which do not have these blind spots, for lesion localization and identification holds significant clinical importance. The use of artificial intelligence (AI) technology promises to enhance the sensitivity, accuracy, and speed of diagnosing small intestinal polyps. In light of this, this review analyzed the latest research advancements in the application of artificial intelligence technologies for non-invasive detection of small intestinal polyps, including image segmentation, three-dimensional reconstruction of small intestinal polyps, and disease classification and prediction of small intestinal polyps. The aim is to improve the accuracy of non-invasive detection and diagnosis of small intestinal polyps, clarify the technological development context, and provide directions for subsequent research.