Knowledge of natural background levels (NBLs) for distinguishing between naturally high-level compound genesis and anthropogenic contamination is indispensable. Accurately establishing natural background levels of groundwater nitrate in urbanized areas is especially challenging. However, classical approaches have limitations in accurately identifying background samples. In this study, a coupled multi-method approach integrating Cl/Br ratio analysis and the k-nearest neighbor (KNN) algorithm was established to identify nitrate NBLs in different groundwater units of the Pearl River Delta (PRD). The PRD is one of the most densely populated and economically developed regions in China, characterized by rapid and large-scale urbanization, intensive land use change, and widespread anthropogenic disturbances, all of which have complex impacts on groundwater systems. The results showed that the alluvial-proluvial unit (Unit II) had the highest nitrate NBLs at 21.15 mg/L, followed by the fissured unit (Unit III) with a lower concentration of 11.45 mg/L. In contrast, the coastal alluvial unit (Unit I) exhibited the lowest value at 9.55 mg/L. Furthermore, the effectiveness of the method used in this study was validated from multiple perspectives by combining the percentage of polluted index analysis, land-use type distribution comparisons, and socioeconomic parameter correlation tests. It is also worth noting that high ammonium concentrations observed in the PRD, derived from both anthropogenic and geogenic sources, may promote nitrification and contribute to elevated nitrate levels, potentially influencing the assessment of NBLs. Therefore, the Cl/Br–KNN integrated approach provides a new perspective on a method for identifying groundwater nitrate NBLs and can provide a scientific reference for other highly human-impacted aquifer systems worldwide.