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Research contextEstuarine environments face strong environmental pressure and human use; water quality there matters for ecosystems and management decisions. At the same time, there is growing literature using machine learning, deep learning, and artificial intelligence to predict, forecast, or model water-quality variables. That body of work is spread across many journals and databases; a systematic synthesis helps map the state of the art, gaps, and methodological patterns. ObjectiveTo characterize and compile in a transparent, replicable way the set of peer-reviewed articles that link AI / machine learning / deep learning to prediction or modeling of water quality in estuarine settings—supporting qualitative and quantitative analysis of the review and open sharing of metadata via a repository. MethodologyA systematic literature review was conducted. The structured search (Boolean blocks for technology, water-quality task, and “estuary/estuaries”) was run on indexed databases (Scopus, Web of Science, ScienceDirect), with time ranges and field coverage as allowed by each platform. Records were managed in Zotero, with duplicate removal and screening (title/abstract and full text), disagreements resolved by consensus, and explicit inclusion criteria (e.g. estuarine focus; ML/DL/AI used to predict or model water-quality parameters; English language; sufficient methodological detail). Selection is documented (e.g. PRISMA flowchart). The main empirical product in this deposit is the metadata spreadsheet for the final included studies, plus supporting files (search strategy, criteria, review manuscript). Use of the dataThe data support: (1) citing and archiving the review’s study set on Zenodo or similar; (2) bibliometric or secondary analyses (by year, country, journal, keywords); (3) integrating DOIs and abstracts into synthesis tools or reference managers; (4) reproducibility (others can verify or extend the selection); (5) figures, narrative reviews, or future work on gaps (predicted variables, algorithms, spatial/temporal scales). The manuscript PDF complements academic use; the spreadsheet is the core for tabular analysis of included records.