Machine learning (ML) has evolved into an indispensable tool for uncovering hidden patterns and deducing correlations. Currently, ML is having a profound impact on the field of groundwater resources and environment research by enhancing predictive accuracy and optimizing management strategies. In this study, we conducted a bibliometric review using CiteSpace and a global-scale analysis of ML methods applied to groundwater resources and quality based on 1326 records. The findings suggest that ML applications in groundwater resources and water environment research are still in their infancy compared with other environmental science fields. This paper then provides a systematic summary of the specific applications of machine learning methodologies within groundwater research, focusing primarily on the prediction of groundwater levels and water quality, along with the extraction of feature importance. Furthermore, a comparison was made of the pros and cons of several prevalent ML techniques used in groundwater level and water quality studies, with an emphasis on the significance of aligning data with models during the application of ML. Finally, the challenges encountered by ML tools in groundwater research were addressed, along with opportunities for the future. The significant potential of employing ML methodologies in groundwater is proposed to make the invisible visible.

Source apportionment at contaminated sites often relies on shallow samples, whereas remediation decisions concern a three-dimensional contamination body. We tested the within-site shallow-to-profile transferability of source-associated heavy-metal fingerprints using 186 soil samples from 43 boreholes reaching approximately 8 m at a former phosphate-fertilizer and leather-production site. Stage 1 positive matrix factorization (PMF) used 37 shallowest source-proximal samples to define four factors; leave-one-borehole-out (LOBO) and separately designed five-strip spatial holdouts evaluated their reconstruction of unseen samples. A five-factor Stage 2 model characterized the full-profile receptor domain. All eight metal concentrations decreased in site-wide depth trends, whereas held-out reconstruction error showed no detected systematic increase with depth (LOBO slope of log10(Qi), 0.012 m⁻¹; 95% CI, −0.022 to 0.049). Cr-, Cu–Ni–Zn-, and Hg-associated factor families remained recognizable across stages. The shallow As–Cd–Pb-associated structure had a composite correspondence (cosine similarity 0.970) with two full-profile factors enriched in Cd and As–Pb; this statistical resolution was not independently corroborated as element-specific transport or physical-source separation. At approximately 8 m, factor median contributions ranged from 0.507 to 0.863 of their shallow medians. Local deep Cr maxima at S35, S42, and S43 coexisted with relatively small fixed-basis Cr residuals. These findings separate fingerprint reconstruction fidelity, contribution persistence, and localized enrichment, supporting a conditional—not universal—use of shallow factor bases when prioritizing deep sampling and independent source verification.