Compared　to　liquid　electrolytes,　lithium　solid-state　electrolytes　have　received　increased　attention　in　the　field　of　all-solid-state　lithium　ion　batteries　due　to　safety　requirements　and　higher　energy　density.　However,　solid-state　electrolytes　face　many　challenges,　including　lower　ionic　conductivity,　complex　interfaces,　and　unstable　physical　or　electrochemical　properties.　One　of　the　most　effective　strategies　is　to　find　a　new　type　of　lithium　solid-state　electrolyte　with　improved　properties.　Traditional　trial　and　error　methods　require　resources　and　time　to　verify　the　new　solid-state　electrolytes.　Recently,　new　lithium　solid-state　electrolytes　were　predicted　through　machine　learning　(ML),　which　has　proved　to　be　an　efficient　and　reliable　method　for　screening　new　functional　materials.　This　paper　reviews　the　lithium　solid-state　electrolytes　that　have　been　discovered　based　on　ML　algorithms.　The　selection　and　preprocessing　of　datasets　in　ML　technology　are　initially　discussed　before　describing　the　latest　developments　in　screening　lithium　solid-state　electrolytes　through　different　ML　algorithms　in　detail.　Lastly,　the　stability　of　candidate　solid-state　electrolytes　and　the　challenges　of　discovering　new　lithium　solid-state　electrolytes　through　ML　are　highlighted.　©　Copyright　©　2021　Liu,　Ma,　Wu,　Wang　and　Wang.