The　purpose　of　this　study　is　to　investigate　the　relationship　between　chronic　pain　and　the　mortality　rate　of　cerebrovascular　disease　(CVD)　in　cancer　patients.　Thus,　we　performed　a　case-control　investigation　was　conducted　by　utilizing　data　from　the　Surveillance,　Epidemiology,　and　End　Results　(SEER)　database　between　1975　and　2019.　Multiple　demographics,　pain　rating　and　other　clinical　characteristics　were　extracted　to　assess　predictors　for　the　death　from　CVD　in　cancer　patients.　Different　machine　learning　algorithms　were　applied　to　construct　pain-related　prediction　model.　The　analysis　involved　16,850　case　patients　and　710,729　controls.　Among　cancer　patients,　approximately　2.3%　succumbed　to　subsequent　CVD.　Cancer　pain　(Pain　rating　II)　was　associated　with　a　decreased　risk　of　CVD.　Univariate　and　multivariate　COX　analyses　indicated　that　older　age　at　cancer　diagnosis,　male　gender,　single　marital　status,　Black　or　Other　race,　and　lack　of　systemic　therapy　correlated　with　a　higher　risk　of　CVD-related　death.　Propensity　score　matching　revealed　a　significantly　lower　proportion　of　Pain　rating　II　in　the　case　group.　The　logistic　regression　algorithm　demonstrated　superior　predictive　ability　for　5-year　and　10-year　CVD　risk　in　cancer　patients.　Notably,　survival　time,　age,　and　pain　rating　emerged　as　the　top　three　crucial　variables.　This　study　firstly　investigated　pain　and　various　risk　factors　for　CVD　in　cancer　patients,　highlighting　pain　as　a　novel　and　possible　protective　factor　for　CVD.　The　development　of　a　risk　model　based　on　pain　could　aid　in　identifying　individuals　at　high　risk　for　CVD　and　may　inspire　innovative　strategies　for　preventing　CVD　in　cancer　patients.