Abstract: Groundwater Hydrochemical Background Levels (GHBLs) are recognized as a fundamental tool for identifying groundwater contamination and have been widely applied in research on hydrogeology, contaminated hydrogeology, and eco-hydrogeology. This paper systematically reviews the conceptual basis and assessment framework of GHBLs, summarizes key issues in their determination, evaluates approaches for assessing result reliability, and highlights future research directions. Unlike traditional “natural background levels,” GHBLs reflect not only hydrochemical characteristics governed by natural processes but also the legacy effects of long-term anthropogenic activities. The determination of GHBLs generally involves three critical stages: statistical unit delineation, sampling design, and anomaly identification. Statistical units are commonly established based on hydrogeological zoning and may be further refined using hydrogeochemical characteristics; however, standardized quantitative criteria for delineation remain lacking. Sampling design is mainly assessed in terms of spatial representativeness and minimum sample size, although related methodological studies remain limited. Existing anomaly identification methods include preselection, statistical, hydrogeochemical, and influencing-factor-based approaches, each with its own strengths and limitations. Combining multiple methods can improve the robustness and reliability of anomaly identification. However, establishing a unified framework applicable across different regions remains challenging, and anomaly identification for trace constituents is still difficult. Current validation of GHBL rationality mainly relies on interpreting anomaly sources and identifying hydrogeochemical controls, yet anomaly attribution remains largely qualitative and subject to uncertainty. Future research should strengthen the quantitative evaluation of statistical-unit delineation and sampling design, develop assessment frameworks adaptable to different hydrogeological settings, expand the application of machine learning in anomaly identification, and promote multi-source data integration.