Artificial intelligence is transforming donor-recipient matching in abdominal organ transplantation by integrating complex datasets—such as clinical variables, imaging, genomics, and immunologic profiles—into predictive models that can optimize graft allocation and improve outcomes. Machine learning techniques like gradient boosting, random forests, and deep neural networks have demonstrated superior accuracy over traditional scoring systems in predicting graft survival, organ usability, and rejection risk. Studies in liver and pancreas transplantation have shown AI’s ability to personalize matching decisions, reduce organ discard rates, and potentially increase graft longevity. However, challenges remain in terms of data quality, model interpretability, bias mitigation, and clinical adoption. As ongoing research and policy efforts converge, AI-driven allocation systems may soon enhance both fairness and utility in abdominal transplant programs.