Rapid urbanization has made people's need for scientific quantification and spatial optimization of urban ecosystem services more urgent, thus helping maintain environmental sustainability. This study integrates the spatial analysis of Geographic Information Systems (GIS) with two ecological models, InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) and ARIES (Artificial Intelligence for Ecosystem Services), to offer a comprehensive framework for assessing and improving urban ecosystem services. The study focuses on key ecological elements such as urban green areas, water bodies, and forest land, and quantifies the critical ecosystem services they provide, namely carbon sequestration, water conservation, and biodiversity maintenance. The InVEST model is used to estimate biophysical services like carbon storage and water yield based on process algorithms, while the ARIES model employs machine learning to assess biodiversity maintenance by modeling habitat-species relationships. GIS is utilized for spatial data pre-processing, overlay analysis, and scenario modeling, which allows for the identification of ecosystem service ‘hot spots’ (high-value areas) and ‘cold spots’ (low-value areas) within a 1,200 km2 urban cluster.