Carbon　dioxide　emissions　have　become　a　pressing　global　issue,　commanding　widespread　attention.　Metal-organic　frameworks　(MOFs)　are　recognized　as　one　of　the　most　promising　materials　for　CO2　capture.　Computational　simulations　have　been　broadly　employed　to　explore　and　delineate　the　mechanisms　underlying　CO2　capture　within　MOFs.　This　manuscript　summarizes　the　most　recent　advances　of　MOF-based　computational　simulations　on　CO2　capture　over　the　past　six　years.　Firstly,　the　computational　simulation　methodologies　and　models　are　introduced.　In　the　following　sections,　the　strategies　and　attempts　for　enhancing　CO2　adsorption　and　selective　separation　in　MOFs　are　summarized,　along　with　the　effects　of　pure　MOF　membrane　and　MOF-based　mixed　matrix　membranes　(MMMs).　Finally,　the　potential　techniques　such　as　machine　learning　(ML)　and　artificial　intelligence　model　ChatGPT,　as　well　as　their　potential　application　and　challenges　in　the　design　and　discovery　process　of　MOF　is　prospected.