Identifying the driving factors of urban landscape pattern changes is key to better understanding the dynamic patterns, processes and influences of urban landscapes, which is crucial for optimizing and predicting of urban landscape patterns and for formulation of urban spatial planning and policies. We systematically reviewed the drivers of changes in urban landscape patterns, generally divided into human activities and natural factors. Human activities, especially population change, economic development, and policies, are the primary driving forces of urban landscape pattern changes, particularly over shorter time scales. Natural factors, such as topography, climate, and water resources, provide the material foundation and environmental conditions shaping urban landscape patterns, primarily determining the spatial pattern of cities over long periods. In addition, we summarized the quantitative analysis models of the drivers of urban landscape pattern change, including empirically based statistical models and process-based dynamic models. Statistical models dominate in the quantitative analysis of driving factors of urban landscape change and are classified according to their correlation relationships into linear, non-linear relationship, spatial relationship and causality models. Process-based dynamic models, including the system dynamics model, cellular automata model, and multi-agent system model, effectively simulate the operation of systems by deeply understanding various driving forces and analyzing interactions among the internal components of the system. Furthermore, we clarified the spatio-temporal and scaling heterogeneity patterns of driving factors of urban landscape pattern change at different scales, including global, national, agglomeration and city levels, as well as the direct and indirect effects of different driving factors. The degree and direction of influence of drivers of urban landscape pattern change vary regularly over time, differ between regions, and change with the spatial scale of the study unit. Moreover, these drivers are not isolated from each other, and there are complex interactions between them. Changes in these drivers can directly affect the spatial configuration of urban landscapes, and the interactions between different factors can also have indirect effects. Finally, we identified future research directions for the study of driving factors of urban landscape pattern change, including temporal attribution analysis of urban landscape pattern change, spatial effects and feedback mechanisms of urban social-ecological landscape patterns and their driving factors, and cross-scale interaction analysis of driving factors influencing urban landscape pattern change.