Under　the　background　of　global　warming　and　the　energy　crisis,　the　Chinese　government　has　set　the　goal　of　carbon　peaking　and　carbon　neutralization.　With　the　rapid　development　of　machine　learning,　some　advanced　machine　learning　algorithms　have　also　been　applied　to　the　control　and　prediction　of　carbon　emissions　due　to　their　high　efficiency　and　accuracy.　In　this　paper,　the　current　situation　of　machine　learning　applied　to　carbon　emission　prediction　is　studied　in　detail　by　means　of　paper　retrieval.　It　was　found　that　machine　learning　has　become　a　hot　topic　in　the　field　of　carbon　emission　prediction　models,　and　the　main　carbon　emission　prediction　models　are　mainly　based　on　back　propagation　neural　networks,　support　vector　machines,　long　short-term　memory　neural　networks,　random　forests　and　extreme　learning　machines.　By　describing　the　characteristics　of　these　five　types　of　carbon　emission　prediction　models　and　conducting　a　comparative　analysis,　we　determined　the　applicable　characteristics　of　each　model,　and　based　on　this,　future　research　ideas　for　carbon　emission　prediction　models　based　on　machine　learning　are　proposed.