Liver diseases—MASLD, HCC, viral hepatitis, cirrhosis—pose major global health challenges. Traditional diagnostics (biopsy, imaging, lab tests) suffer from sampling error, inter-observer variability, and limited sensitivity, delaying early detection and optimal treatment selection. Artificial Intelligence-AI (ML-Machine Learning / DL-Deep Learning) offers pattern recognition and predictive modeling that can revolutionize hepatology. We conducted a narrative scoping review (Jan 2020–Aug 2026) drawing on primary studies identified via PubMed, Scopus, and Google Scholar, together with existing peer-reviewed systematic reviews and meta-analyses that have already pooled quantitative performance data for specific AI-in-hepatology applications. Pooled deep-learning models for hepatocellular carcinoma detection on medical imaging achieve sensitivity of 89% (95% CI 87–91) and AUC of 0.95 (95% CI 0.93–0.97) across 30 pooled studies. AI-assisted detection of hepatic steatosis pools to sensitivity of 91% (95% CI 84–95) and AUC of 0.97. Pooled AUC for advanced liver fibrosis is 0.92 and for cirrhosis 0.85 (19 studies). In liver transplantation, ML models consistently outperformed legacy scoring systems (MELD, BAR, SOFT, DRI) in the studies reviewed; the strongest available pooled comparative evidence (9 studies, 18,771 transplants) reports an AUROC of 0.82 for the best model versus the BAR score, notably lower than best-case single-study claims elsewhere in the literature. AI-driven systems show genuine, meta-analytically supported promise for liver disease diagnostics and transplant prognostication, but heterogeneity in study design and validation practice currently limits direct comparison across algorithms. Priorities include standardized external validation, explainable AI, and prospective clinical trials.