Suzhou, one of the most developed cities in Yangtze Delta, lies by Taihu lake in Jiangsu province, China. Because the city’s economic development has been rocketing upward in the past 20 years, it is necessary to assess the influence on natural resources exerted by socio-economic growth. Ecological footprint (EF) is one of the sustainable development assessment indicators. How to simulate the EF’s development trend of the past in a given region for a long time is a question to be solved. This paper calculates the total ecological footprint of Suzhou from 1990 to 2009, and attempts to simulate the total ecological footprint (TEF) of the city using the back propagation artificial neural network (BPANN) model, a widely used modeling approach fitting nonlinear time series in artificial neural networks. Seven socioeconomic factors: gross domestic product, tertiary industrial products, secondary industrial products, urban population, rural population, annual income of rural residents per capita, and annual income of city dwellers per capita acted as drivers of the TEF in the quantitative analysis. The fitting performance of the model was accurate and TEF of the city from 1990 to 2009 could be simulated by a model. With the proposed approach in this study, the ecological sustainability of Suzhou could be analyzed.