Focusing on high cost and complicated process of the fine particulate matter (PM _2.5) measurement system, a neural network model based on grey wolf optimizer algorithm was established. From the perspective of non-mechanism model, the daily PM _2.5 concentration in Shanghai was forecasted with meteorological factors and air pollutants, and the important factors were analyzed by mean impact value. To avoid the "over training" and ensure the generalization ability, the validation datasets were used to monitor the training process. The experimental results show that the most significant factors that affecting the PM _2.5 concentration are PM ₁₀, and then are the CO and the previous day's PM _2.5. Based on the datasets obtained from November 1, 2016 to November 12, the relative average error of the proposed model is 13.46%, the absolute average error is 8μg/m ³; the relative average error of it is decreased by about 3 percentage points, 5 percentage points and 1 percentage points compared with the prediction models based on Particle Swarm Optimization (PSO), BP neural network and Support Vector Regression (SVR). The neural network model based on the grey wolf optimizer algorithm is more suitable for forecasting PM _2.5concentration and air quality in Shanghai.