Background: Current prioritization models for liver transplantation (LT) are hampered by their linear nature, which does not fully capture the severity of patients with extreme analytical values. Methods: Cohort study including adult patients who qualified for elective LT in the United Kingdom (2010-2020, model training and internal validation) and in two Australian institutions (1998-2020, external validation). The Gender-Equity model for Liver Allocation corrected by serum sodium (GEMA-Na) was compared with a shallow artificial neural network optimized by neuroevolution and hybridization (GEMA-AI) using the same input variables. The primary outcome was mortality or delisting for sickness within the first 90 days. Discrimination was assessed by Harrell’s c-statistic (Hc). Results: The study population comprised 9,320 patients: training cohort n=5,762, internal validation cohort n=1,920, and external validation cohort n=1,638. The prevalence of 90-days mortality or delisting for sickness ranged from 5.3% to 6% in the different cohorts. The transition from a linear to a non-linear score (from GEMA-Na to GEMA-AI) resulted in improved discrimination in the internal and external validation cohorts (Hc=0.766 vs Hc=0.781; p=0.035 and Hc=0.774 vs Hc=0.793; p=0.003, respectively), being these differences more pronounced in women (Hc=0.802 vs Hc=0.826; p=0.048 and Hc=0.796 vs Hc=0.836; p=0.002, respectively). Among 1,403 patients (39.4% of the merged validation cohorts) who showed at least one extreme analytical value, GEMA-AI had Hc=0.823 compared to Hc=0.797 (p=0.036). A meaningful change ≥2 score prioritization points occurred in 27.8% of patients (11.4% upgraded, 16.4% downgraded). Differential prioritization would occur in 6.4% of the available organs within the first 90 days and would save one in 59 deaths overall, and one in 13 deaths among women. Conclusions: The use of non-linear explainable machine learning models may improve predictions of waiting list outcomes, particularly in the sickest patients showing extreme analytical values. Their use should be preferred over Cox’s regression-based models.