This　study　explores　the　transformative　role　of　artificial　intelligence　(AI)　and　machine　learning　(ML)　in　materials　science,　leveraging　large　language　models　(LLMs)　such　as　OpenAI＇s　ChatGPT.　Focusing　on　(oxy)hydroxides　as　oxygen　evolution　reaction　(OER)　catalysts,　we　demonstrate　how　LLMs　streamline　data　extraction,　significantly　reducing　reliance　on　traditional,　time-intensive　methods.　Using　few-shot　training　and　strategic　prompting,　ChatGPT　achieved　an　extraction　accuracy　of　approximately　0.9.　The　curated　data　set　was　then　used　to　predict　OER　performance　via　the　PyCaret　library　to　evaluate　various　ML　algorithms　and　a　high-accuracy　XGBoost　regression　model　with　accuracies　above　0.9　is　subsequently　established.　Further　analysis　using　SHAP　and　Python　Symbolic　Regression　(PySR)　identified　key　descriptors-electrochemical　double-layer　capacitance,　transition　metal　composition,　support　material,　and　d-electron　count-as　critical　factors,　consistent　with　established　electrochemical　principles.　Additionally,　SHAP＇s　extreme　values　for　Cu　and　Zn　suggest　unconventional　catalytic　roles,　potentially　linked　to　Cu2O-facilitated　NiOOH　formation　and　Zn-induced　electronic　modulation,　demonstrating　the　power　of　data-driven　analysis　in　uncovering　hidden　mechanisms.　To　enhance　literature-based　insights,　Microsoft＇s　GraphRAG　technology　was　employed　for　in-depth　chemical　information　retrieval.　Overall,　this　study　introduces　an　innovative,　end-to-end　ML　framework　powered　by　ChatGPT,　promoting　broader　AI　adoption　in　scientific　research　and　bridging　computational　intelligence　with　experimental　sciences.