Reliability　and　maintenance　(RM)　engineering　is　conventionally　notorious　for　a　lack　of　sufficient　failure　data　to　develop　robust　statistical　models.　The　increasing　miniaturization　of　data　collection　devices　such　as　wireless　sensors　has　provided　a　promising　infrastructure　for　gathering　information　about　parameters　of　the　physical　systems,　which　enable　practitioners　and　researchers　to　apply　machine　learning　(ML)　algorithms　to　improve　the　efficiency　of　RM　analysis.　The　number　of　published　papers　on　ML　in　RM　is　enormous,　this　paper　will　therefore　categorizes　those　papers　that　were　published　between　2017　to　16/May/2020,　that　are　written　in　English,　that　have　received　a　top　5%　number　of　citations　in　the　year　published,　and　that　use　support　vector　methods,　random　forests,　and　cluster　analysis.　©　2020　IEEE.