The aim of this study was to establish a method for predicting heavy metal concentrations in PM_2.5 (particulate matter with a diameter of less than 2.5 μm) using support vector machine (SVM) models combined with magnetic properties of leaves. In this study, PM_2.5 samples and the leaves of three common evergreen tree species were collected simultaneously during four different seasons in Nanjing, China. A SVM algorithm was used to establish models for the prediction of airborne heavy metal concentrations based on leaf magnetic properties, with or without meteorological factors and pollutant concentrations as input variables. Results showed that the annual average PM_2.5 concentration was 58.47 μg/m³. PM_2.5 concentrations, leaf magnetic properties, and nearly all airborne heavy metals had higher concentrations in winter than in spring, summer, or fall. Ferrimagnetic minerals preponderant in dust-loaded leaves were sampled from the three tree species. Models using magnetic properties of leaves from Ligustrum lucidum Ait and Osmanthus fragrans Lour yielded better prediction effects than those based on the leaves of Cedar deodara G. Don, showing relatively higher correlation coefficient (R) values and lower errors in both training and test stages. Fe and Pb concentrations were well-simulated by the prediction models, with R values > 0.7 in both training and test stages. By contrast, the concentrations of V, Co, Sb, Tl, and Zn were relatively poor-simulated, with most R values < 0.7 in both training and test stages. Predictions for the main urban areas of Nanjing showed that the highest heavy metal concentrations occurred near industrial and traffic pollution sources. Our results provide a cost-effective approach for the prediction of airborne heavy metal concentrations based on the biomagnetic monitoring of tree leaves.