Objectives. This review evaluates and summarizes current evidence on the use of artificial intelligence (AI) in gastrointestinal (GI) endoscopy and digestive disease management. We highlight recent advances in AI-based image analysis, diagnostic accuracy, and clinical use while identifying challenges such as standardization, education, and ethical issues. Methods. We conducted a structured literature review of AI in GI endoscopy, focusing on publications from 2023 to 2025. We searched databases like PubMed and EMBASE using the following terms: “artificial intelligence,” “machine learning,” and “gastrointestinal endoscopy.” We included systematic reviews, meta-analyses, clinical trials, consensus statements, and narrative reviews. Studies were selected and reviewed according to PRISMA principles, focusing on their diagnostic outcomes, technology features, and quality assessments. Results. AI-enhanced endoscopy has significantly improved lesion detection compared to standard endoscopy. For instance, AI-assisted colonoscopy increases the adenoma detection rate by about 20% and lowers miss rates by around 55%. AI models demonstrate high sensitivity and specificity for upper GI neoplasms, such as early esophageal and gastric cancer, and Helicobacter pylori infection. Recent society guidelines recognize AI's role, with the ASGE listing “AI in endoscopy” as a top research topic, and recommend standardized evaluation through QUAIDE consensus. Explainable AI tools are being developed to tackle concerns about the "black box" nature of these systems. Other applications include capsule endoscopy, where AI assesses bowel cleanliness more consistently than human raters, and inflammatory bowel disease (IBD), where AI combines multi-omic data for disease phenotyping. However, challenges persist, including the risk of operator deskilling after extended AI use, data bias, cost-effectiveness, and regulatory barriers. Recent reviews highlight ethical issues related to patient privacy and dataset diversity. Conclusions. AI is quickly transforming GI endoscopy by improving diagnostic accuracy and efficiency. To promote clinical adoption, we need rigorous validation and standardization, as suggested by QUAIDE, along with strategies for integrating AI into endoscopist training while maintaining human expertise. Future efforts should focus on large multicenter trials, explainable algorithms, and integrating AI into clinical workflows.