ObjectiveTo review the methodological evolution, applicable scenarios, and HTA use of next-generation population-adjusted indirect comparison methods. MethodsBuilding on traditional indirect comparisons and first-generation population-adjusted methods, this review summarizes four major development paths—weight optimization, unified modelling, marginalization, and subgroup interpolation—and discusses the principles, assumptions, strengths, and limitations of two-stage matching-adjusted indirect comparison (2SMAIC), multilevel network meta-regression (ML-NMR), G-computation, multiple imputation marginalization (MIM), and network meta-interpolation (NMI). Publicly available HTA cases were also examined to identify major appraisal concerns. ResultsCompared with traditional matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC), these next-generation methods better address complex evidence networks, transportability to target populations, and incompatibility between conditional and marginal effects. ML-NMR is most useful for anchored evidence networks with multiple studies; 2SMAIC mainly improves precision within the MAIC framework; G-computation and MIM obtain decision-relevant marginal effects through model-based standardization; and NMI enables limited adjustment using subgroup results when individual patient data are unavailable. Published applications remain concentrated in pharmaceuticals. HTA agencies are cautiously open to these methods, but focus on target-population relevance, covariate overlap, key assumptions, and uncertainty analyses. ConclusionsNext-generation population-adjusted indirect comparison methods substantially expand the toolkit for evidence generation when head-to-head trials are unavailable, but they do not remove the dependence on data quality and methodological assumptions. Method choice should therefore be aligned with the evidence structure, data availability, and decision question, with transparent reporting and sensitivity analyses.