Achieving sustainable urban development during rapid urbanization is one of the important issues worldwide. Eco-economic regionalization (EER) is a complex regionalization method for dividing urban regions into different eco-economic functional zones by considering a wide range of local environmental (water-heat condition, biodiversity, vegetation coverage rate, etc.) and socio-economic (gross domestic product (GDP), population density, human activity pressure, road network density, etc.) factors. Urban and regional planning based on EER might be beneficial for local environmental protection, as well as sustainable economic growth. Previous studies have investigated the principles, methods, and index system for EER application. However, improving the accuracy and dynamic adaptability of EER is essential in order to ensure its application to small urban regions. In this study, the EER approach was modified by simulating population density in urban areas of Zengcheng District, Guangzhou, and an adaptive regionalization model was developed using remote sensing (RS) and geographical information system (GIS) techniques. Improving the accuracy of raw input data for calculating the indices is important to ensure the accuracy of the EER results. Previously, population density was aggregated on an administrative regional basis, whereas, in this study, we estimated population density grid by grid within the built-up area of Zengcheng District by linear modeling of the gray value in each pixel from the Defense Meteorological Satellite Program/Operational Linescan System nighttime light data. According to the linear model, the total number aggregated from all these pixels was equal to the statistical population in each administrative region by assuming that nobody lives outside the built-up area. Further, an Adaptive Eco-economic Regionalization model (AEER) was developed for Zengcheng District in order to enhance the dynamic adaptability of the EER approach. In our AEER model, the study area was first divided into four zones-ecological conservation zone, ecological priority zone, development optimization zone, and key development zone-allowing the comparison of EER results across various case study areas. Next, the area and location of each of the four zones were determined by introducing six parameters into the AEER model so that the results could be more adaptive to local management objectives. Further, two scenarios were developed for the application of this AEER model in Zengcheng District. Our results indicated that the AEER model yields highly accurate zoning results that are more adaptive to the local context. Therefore, this model might be a powerful tool for urban and regional EER applications in other city areas. Further, three perspectives have been proposed on improving the current EER model:(1) Producing spatially explicit input data for index estimation (such as GDP and available resources, both of which are normally aggregated on administrative regional basis) is the key to improving the accuracy of the zoning results. (2) The results from the EER model are only useful when the zoning process is more adaptive to local management objectives. (3) The zoning results should be presented for each administrative region in order to obtain strong policy implications; further, they should be presented grid by grid so that the vital ecological processes can be better preserved based on this EER approach.