Background　The　predictive　importance　of　the　stress　hyperglycemia　ratio　(SHR),　which　is　composed　of　admission　blood　glucose　(ABG)　and　glycated　hemoglobin　(HbA1c),　has　not　been　fully　established　in　noncardiac　surgery.　This　study　aims　to　evaluate　the　association　and　predictive　capability　the　SHR　for　major　perioperative　adverse　cardiovascular　events　(MACEs)　in　noncardiac　surgery　patients.　Methods　Individuals　who　underwent　noncardiac　surgical　procedures　between　2011　and　2020,　including　both　diabetic　and　non-diabetic　patients,　were　identified　in　the　perioperative　medicine　database　(INSPIRE　1.1)　and　classified　into　tertiles　based　on　their　SHR.　The　connection　between　the　SHR　and　the　risk　of　MACEs　was　studied　using　Cox　proportional　hazards　regression　analysis,　then　restricted　cubic　spline　(RCS)　was　employed　to　assess　the　association＇s　form.　Additionally,　the　SHR＇s　incremental　predictive　utility　for　MACEs　was　assessed　by　the　C-statistic,　continuous　net　reclassification　improvement　(NRI),　and　integrated　discrimination　improvement　(IDI),　thereby　quantifying　the　enhancement　in　predictive　accuracy　brought　by　incorporating　the　SHR　into　existing　risk　models.　Feature　importance　and　predictive　models　were　generated　utilizing　the　Boruta　algorithm　and　machine　learning　approaches.　Results　A　total　of　5609　patients　were　enrolled.　With　an　upwards　shift　in　SHR　vertices,　the　rate　of　perioperative　MACEs　and　cardiac　death　event　steadily　rose.　The　RCS　analysis　for　perioperative　MACEs　and　cardiac　death　event　both　indicated　J-shaped　associations.　Inflection　points　occurred　at　SHR　=　0.81　for　MACEs　and　SHR　=　0.97　for　cardiac　death.　The　model＇s　fit　improved　significantly,　with　a　continuous　NRI　of　0.067　(95%　CI:　0.025-0.137,　P　＜　0.001)　and　an　IDI　of　0.305　(95%　CI:　0.155-0.430,　P　＜　0.001).　When　SHR　was　added　as　a　categorical　variable　(＞　0.81),　the　C-statistic　increased　to　0.785　(95%　CI:　0.756-0.814)　with　a　Delta　C-statistic　of　0.035　(P　=　0.009),　a　continuous　NRI　of　0.007　(95%　CI:　0.000-0.021,　P　=　0.016),　and　an　IDI　of　0.076　(95%　CI　-0.024-0.142,　P　=　0.092).　In　the　Boruta　algorithm,　variables　identified　as　important　features　in　the　green　area　were　incorporated　into　the　machine　learning　models　development.　Conclusions　The　SHR　was　related　with　an　increased　risk　of　perioperative　MACEs　in　patients　following　noncardiac　surgery,　highlighting　its　potential　as　a　useful　and　reliable　predictive　tool　for　assessing　the　risk　of　perioperative　MACEs.