The　plastic　rotation　angle　or　deflection　is　typically　used　as　the　indicator　to　evaluate　the　earthquake-induced　damage　and　classify　the　seismic　performance　levels　of　reinforced　concrete　(RC)　beams.　Herein,　one　of　the　most　important　issues　is　to　determine　the　seismic　performance　level　limits　of　RC　beams.　Whereas,　different　countries　provided　their　specific　method　to　determine　the　limit　values　by　considering　the　loading-carrying　capacity　of　RC　beams,　which　could　not　be　used　to　describe　the　earthquake-induced　seepage　of　structures,　especially　for　underground　structures.　Therefore,　in　this　study,　the　seismic　performance　level　limits　of　RC　beams　were　predicted　by　using　the　machine　learning　methods　and　considering　the　development　of　cracks.　Firstly,　the　seismic　performance　level　limits　of　RC　beams　were　presented　after　discussing　the　methods　in　different　codes　and　the　development　of　cracks.　Then　an　earthquake　performance　test　database　of　RC　beams　was　established　after　collecting　452　test　results　of　RC　beams,　and　Pearson　correlation　analysis　was　conducted　for　feature　selection　to　determine　the　input　mechanical　parameters　and　dimensional　parameters　for　machine　learning.　Meanwhile,　the　correlation　between　the　inputs　and　limit　values　was　analyzed　using　the　mutual　information　method.　Regression　models　of　seven　machine　learning　methods　were　then　established　to　predict　the　performance　level　limits　of　RC　beams,　and　the　hyperparameters　of　the　machine　learning　models　were　optimized　with　the　TPE　optimization　algorithm　and　cross-validation.　The　generalization　ability　of　the　prediction　models　was　evaluated　and　the　accuracy　of　predicted　results　by　different　methods　was　analyzed.　Finally,　the　predicted　seismic　performance　level　limits　of　RC　beams　could　be　used　to　evaluate　the　earthquake-induced　damage　of　RC　beams　by　combining　them　with　the　seismic　behavior　of　RC　beams.