Chromium　and　its　compounds　are　widely　used　in　many　industries　in　China　and　play　a　very　important　role　in　the　national　economy.　At　the　same　time,　heavy　metal　chromium　pollution　poses　a　great　threat　to　the　ecological　environment　and　human　health.　Therefore,　it＇s　necessary　to　safely　and　effectively　remove　the　chromium　from　pollutants.　In　practice,　there　are　many　factors　which　influence　the　removal　efficiency　of　the　chromium.　However,　few　studies　have　investigated　the　relationship　between　multiple　factors　and　the　removal　efficiency　of　the　chromium　till　now.　To　this　end,　this　paper　uses　the　green　synthetic　iron　nanoparticles　to　remove　the　chromium　and　investigates　the　impacts　of　multiple　factors　on　the　removal　efficiency　of　the　chromium.　A　novel　model　that　maps　multiple　given　factors　to　the　removal　efficiency　of　the　chromium　is　proposed　through　the　advanced　machine　learning　methods,　i.e.,　XGBoost　and　random　forest　(RF).　Experiments　demonstrate　that　the　proposed　method　can　predict　the　removal　efficiency　of　the　chromium　precisely　with　given　influencing　factors,　which　is　very　helpful　for　finding　the　optimal　conditions　for　removing　the　chromium　from　pollutants.