In　the　age　of　digitalization　and　big　data,　cooling　systems　in　data　centers　are　vital　for　maintaining　equipment　efficiency　and　environmental　sustainability.　Although　many　studies　have　focused　on　the　classification　and　optimization　of　data　center　cooling　systems,　systematic　reviews　using　bibliometric　methods　are　relatively　scarce.　This　review　uses　bibliometric　analysis　to　explore　the　classifications,　control　optimizations,　and　energy　metrics　of　data　center　cooling　systems,　aiming　to　address　research　gaps.　Using　CiteSpace　and　databases　like　Scopus,　Web　of　Science,　and　IEEE,　this　study　maps　the　field＇s　historical　development　and　current　trends.　The　findings　indicate　that,　firstly,　the　classification　of　cooling　systems,　optimization　strategies,　and　energy　efficiency　metrics　are　the　current　focal　points.　Secondly,　this　review　assesses　the　applicability　of　air-cooled　and　liquid-cooled　systems　in　different　operational　environments,　providing　practical　guidance　for　selection.　Then,　for　air　cooling　systems,　the　review　demonstrates　that　optimizing　the　design　of　static　pressure　chamber　baffles　has　significantly　improved　airflow　uniformity.　Finally,　the　article　advocates　for　expanding　the　use　of　artificial　intelligence　and　machine　learning　to　automate　data　collection　and　energy　efficiency　analysis,　it　also　calls　for　the　global　standardization　of　energy　efficiency　metrics.　This　study　offers　new　perspectives　on　the　design,　operational　optimization,　and　performance　evaluation　of　data　center　cooling　systems.