Rational　design　of　ionic　liquids　(ILs),　which　is　highly　dependent　on　the　accuracy　of　the　model　used,　has　always　been　crucial　for　CO2　separation　from　flue　gas.　In　this　study,　a　support　vector　machine　(SVM)　model　which　is　a　machine　learning　approach　is　established,　so　as　to　improve　the　prediction　accuracy　and　range　of　IL　melting　points.　Based　on　IL　melting　points　data　with　600　training　data　and　168　testing　data,　the　estimated　average　absolute　relative　deviations　(AARD)　and　squared　correlation　coefficients　(R2)　are　3.11%,　0.8820　and　5.12%,　0.8542　for　the　training　set　and　testing　set　of　the　SVM　model,　respectively.　Then,　through　the　melting　points　model　and　other　rational　design　processes　including　conductor-like　screening　model　for　real　solvents　(COSMO-RS)　calculation　and　physical　property　constraints,　cyano-based　ILs　are　obtained,　in　which　tetracyanoborate　[TCB]-　is　often　ruled　out　due　to　incorrect　estimation　of　melting　points　model　in　the　literature.　Subsequently,　by　means　of　process　simulation　using　Aspen　Plus,　optimal　IL　are　compared　with　excellent　IL　reported　in　the　literature.　Finally,　1-ethyl-3-methylimidazolium　tricyanomethanide　[EMIM][TCM]　is　selected　as　a　most　suitable　solvent　for　CO2　separation　from　flue　gas,　the　process　of　which　leads　to　12.9%　savings　on　total　annualized　cost　compared　to　that　of　1-ethyl-3-methylimidazolium　bis(trifluoromethylsulfonyl)amide　[EMIM][Tf2N].　©　2020　Institute　of　Process　Engineering,　Chinese　Academy　of　Sciences