According to the experimental dataset on the molar magnetic susceptibility χm of 45 alkali metal compounds and the topological descriptor?——magnetic connectivity index mF， which is extracted by the magnetic valence gi of simple ion deduced from classical electrodynamics， support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is proposed to establish a model for predicting the molar magnetic susceptibility of alkali metal compound via 0F and 1F. The performance of SVR model is compared with that of multivariate linear regression （MLR） model. The results show that the mean absolute error， the mean absolute percentage error and the root mean square error for 9-fold cross validation test of SVR models are all smaller than those achieved by MLR models. It is revealed that the generalization ability of SVR model is superior to that of MLR model. This study suggests that magnetic connectivity index is an effective descriptor and the SVR is a powerful approach to the prediction of the molar magnetic susceptibility of alkali metal compounds.