The dynamic simulation and prediction of urban space can provide an important reference for the planning and management of sustainable urban development. The cellular automata model SLEUTH has strong universality and portability in urban spatial simulation. It is based on the Monte Carlo iteration of urban historical data and is capable of automatically identifying urban growth parameters with minimum error, which has effectively resolved the difficulties encountered in determining the conversion rules when using the traditional cellular automata models. In this study, we applied the SLEUTH model to perform urban spatial simulation and prediction under different scenarios in Wuhan City. Our findings revealed that the urban spatial simulation results for the period 2007 to 2011 showed a strong correlation with actual historical data. The Lee-Sallee shape index was greater than 0.6, which proves that the SLEUTH model exhibits a strong universality and suitable simulation accuracy after local correction. Moreover, the urban dynamic changes in Wuhan in 2025 were predicted under three scenarios, namely, the current development trend scenario, the basic protection scenario, and the strict protection scenario. The results of simulation under these three scenarios indicate that urban residential land will increase significantly, whereas agricultural land, woodland, water, and other land would decrease, particularly under the current development trend and basic protection scenarios. Under these two scenarios, considerable decreases are observed in agricultural land, woodland, and water, which may intensify habitat fragmentation, and result in a decline in the quality of cultivated land, water resource shortage, wetland shrinkage, and other ecological problems. Under the strict protection scenario, the rapid proliferation of construction land would be restricted to a large extent and water bodies and woodland would be transformed the least, which would effectively protect natural ecosystem components and maintain sustainable ecosystem services