The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence.

China has entered an era of population decline, yet urbanization continues as rural-to-urban migration persists. This demographic transition has prompted a strategic shift in urban development from extensive spatial expansion toward quality-oriented, intensive growth models. However, evolving human–land supply–demand dynamics in cities historically characterized by population inflows remain insufficiently understood. This study focuses on Xiamen, a prototypical coastal migrant-receiving city, to investigate land use simulation under demographic transition. By integrating the cohort-component method with the Patch-generating Land Use Simulation (PLUS) model, we project Xiamen’s population under three scenarios by 2030: Stable Continuation (SCS), Natural Development (NDS), and National 2030 Population Planning (NPP), with projected increases of 5.56%, 6.76%, and 24.69%, respectively. Results show continued but decelerating population growth, with adequate labor supply and persistent demographic dividend. Notably, the NPP scenario reveals a negative correlation between population growth and construction land expansion. In NPP-High, prioritizing compact development and ecological conservation, population grows by 1.27 million while construction land decreases by 2.85% and forest land increases by 4.09%. This framework provides empirical evidence for compact urban development under the dual constraints of land-use efficiency and ecological protection.