We investigate the eco-environmental effects and the driving factors of transforming the production–living–ecological space (PLES) land use function and offer a scientific foundation for developing regional territorial area and environmental preservation. Eco-environment quality index and ecological contribution ratio are used to analyze the spatial–temporal evolution characteristics and eco-environment effects of land use transformation in the Yangtze River Delta Urban Agglomeration (YRDUA) over the three time periods of 2000, 2010, and 2020, and the geographic detectors are used to analyze the factors that influence the spatial difference of eco-environment quality (EEQ). The findings indicate the following: (1) The land use transformation of YRDUA is primarily shown in the shrinkage of the production land area, the stability of ecological land, and the rapid increase of living land. The area of ecological land, such as water, forest, and pasture, has remained relatively steady from the perspective of secondary land types. In contrast, the area of urban and rural living land has significantly increased. (2) Most land use environment comprises the lower-value zone, accounting for about 50%. The area of the low-value zone has continued to rise owing to the rapid urban and rural living land development, tending to continuous growth. (3) Both the ecological improvement and degradation trends are present simultaneously, although the ecological improvement trend is less prominent than the environmental degradation trend. The primary factor is improving the eco-environment by transforming agricultural production land into forest, water, and ecological pasture land. The degradation of the regional EEQ is mostly due to the occupation of agricultural production land by urban and rural living land. (4) Considering natural elements such as altitude, precipitation, and slope, the extent of land use impacts the EEQ. The combination of several factors has changed the EEQ of the YRDUA. The effect of any two elements is stronger than that of a single factor.