Exploring the urban spatial pattern and expansion characteristics of African countries under shared socioeconomic pathways (SSPs) is crucial to optimizing urban development in Africa and ensuring ecological sustainability. We use land and socioeconomic panel data and the least squares dummy variable regression method to predict the urban land increment in African countries from 2030 to 2060, we use the FLUS model to simulate the urban spatial layout in 2060, and we analyze from the perspective of the relationship between population density and urban expansion. The results show that the urban space of African countries will show a significant expansion trend from 2020 to 2060, with stronger growth under the SSP1 and SSP5 scenarios and relatively weaker growth under the SSP3 scenario; the urban land expansion patterns of different countries under different SSP scenarios are significantly different, and countries with rapid urbanization and economic growth are mostly urban patch agglomeration and extended expansion, while urban patches are relatively evenly distributed; a large number of cities in Africa show specific expansion patterns, with large cities mostly showing loose expansion and small- and medium-sized cities mostly showing compact expansion; and cities in different regions such as North Africa and sub-Saharan Africa have their own expansion characteristics in terms of population density and urban form. Our research provides important data support and inspiration for promoting the rational development of African cities and enhancing regional ecological resilience.

This study develops an integrated multi-algorithm spatial simulation framework for large-scale urban expansion analysis by combining a Back Propagation Artificial Neural Network (BP-ANN), the Future Land Use Simulation (FLUS) model, Cellular Automata (CA), the standard deviational ellipse, Neighborhood Aggregation Analysis, and landscape metrics within a unified data flow. Using the 2020 land use pattern as the baseline, BP-ANN estimates land use suitability probabilities from multiple driving factors; FLUS-CA allocates land under SSP1–SSP5 through adaptive inertia, neighborhood effects, and conversion costs; and spatial statistics and landscape metrics diagnose expansion magnitude, local aggregation, direction, built-up morphology, and cross-country differences. Urban built-up areas expand continuously under all scenarios, increasing by 38.92–49.33% by 2050 relative to 2020. Areas of high local aggregation intensity exhibit pronounced spatial differentiation, while the regional pattern maintains a northwest–southeast orientation, with scenario-dependent differences in centroid location and spatial dispersion. Edge density generally declines as built-up land increases, indicating greater built-up contiguity at the regional scale; this pattern should not be interpreted as evidence of reduced ecological landscape fragmentation. New built-up land encroaches on farmland and coastal ecological spaces and displays marked cross-country heterogeneity. The framework connects driving-factor identification, scenario configuration, spatial allocation, and pattern diagnosis, providing methodological support for cross-national multi-scenario simulation, urban planning, ecological conservation, and transboundary spatial collaborative governance in ASEAN.