Nowadays,　open-pit　mining　is　the　large-scale　extraction　of　valuable　ore　materials　from　the　surface　with　the　use　of　modern　mining　equipment.　If　not　operated　properly,　various　unexpected　events,　such　as　equipment　breakdown,　slope　collapse,　hazardous　gas　emission　and　land　pollution,　would　occur.　With　the　rapid　development　of　computer　technology　and　big-data　science,　emerging　applications　of　machine　learning　could　significantly　improve　mining　predictability,　feasibility,　efficiency　and　sustainability.　However,　there　is　still　a　lack　of　up-to-date　systematic　literature　reviews　on　applications　of　machine　learning　to　open-pit　mining.　To　address　this　issue,　this　study　reviews　over　200　relevant　papers　mainly　published　in　the　last　five　years.　In　this　review,　we　initially　conduct　a　descriptive　statistical　analysis　of　these　papers　according　to　different　phases　in　open-pit　mining.　Consequently,　we　classify　their　research　findings　into　four　main　categories:　exploration,　exploitation,　production　and　reclamation.　In　addition,　each　main　category　is　further　divided　into　some　sub-categories,　namely,　feasibility　evaluation　and　mine　design　planning　in　exploration;　mine　block　sequencing　in　exploitation;　drilling,　blasting,　haulage　and　processing　in　production;　waste　control　and　environmental　protection　in　reclamation.　Based　on　such　a　bi-level　classification,　we　systematically　summarise　promising　machine　learning　techniques　(i.e.　reinforcement　learning　and　deep　reinforcement　learning)　and　potential　research　opportunities　(e.g.　integration　of　machine　learning　and　simulation　for　mining　equipment　scheduling)　in　real-world　implementations　for　the　mining　industry.