Aiming at the problem of large deviation in existing PM2.5 concentration prediction, a novel model based on Improved Firefly Algorithm optimization SVM (IFA-SVM) was proposed. In this model, two neighborhood search strategies and variable step size mechanism were employed to improve FA. The IFA was applied to optimize the SVM parameters (C,, and), and an outstanding model was constructed to forecast PM2.5 concentrations in Taiyuan. The neighborhood search strategies can provide better candidate solutions; search step size was dynamically tuned by using variable step size strategy to accelerate convergence and obtain a trade-off between exploration and exploitation. The performance of the proposed IFA-SVM model has been compared with FA-SVM, Genetic Algorithm (GA)-SVM, and Particle Swarm Optimization (PSO)-SVM. Experimental results show that the proposed IFA-SVM model has achieved more accurate performance for PM2.5 forecasts in 1 day ahead and 3 days ahead compared to other method.