Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions.