The　critical　temperature　is　an　important　parameter　in　the　design　and　selection　of　binary　organic　mixtures.　Rapid　and　accurate　prediction　has　been　a　focus　of　research.　Strong　nonlinear　relationships　exist　between　molecular　characteristics　and　critical　temperature.　Developing　nonlinear　models　is　an　important　measure　to　improve　the　prediction　accuracy.　In　this　paper,　56　different　QSPR　models　are　developed　to　predict　the　critical　temperature　using　14　types　of　mixture　descriptors　and　four　modeling　methods　(MLR,　XGBoost,　RBFNN　and　SVM).　A　dataset　containing　2540　data　points　is　adopted.　Results　show　x1d1+x2d2　is　the　optimum　mixture　descriptor　type,　and　the　accuracy　of　the　nonlinear　models　is　better　than　that　of　the　linear　models.　The　model　combining　x1d1+x2d2　and　SVM　exhibits　the　best　predictive　power;　the　Rext2,　RMSEext,　and　AARD　are　0.988,　10.057　K,　and　1.27%,　respectively.　For　this　model,　78.98%　of　the　data　have　an　absolute　deviation　of　less　than　5　K,　and　the　accuracy　is　better　than　that　of　existing　QSPR　models　for　critical　temperature　of　mixtures.　The　application　domain　analysis　shows　the　model　has　good　performance　for　novel　binary　organic　mixtures.　In　addition,　compared　with　the　empirical　calculation　methods　for　predicting　the　critical　temperature,　the　results　show　the　developed　model　has　higher　reliability.　©　2023