A significant transition is taking place in China’s urban development, which is moving away from rapid expansion and towards improved quality and efficiency. The distribution of primary education resources is thus experiencing a substantial change: transitioning from a traditional emphasis on merely providing school placements to becoming a systematic effort intricately linked with the functional attributes of adjacent urban environments. Nonetheless, current research has not comprehensively examined the relational dynamics between primary schools and adjacent facilities, nor the discrepancies in these dynamics across various cities. This study analyzes nine major Chinese cities as case studies to investigate the compatibility between elementary schools and adjacent infrastructure. It develops a compatibility model through data gathering, feature selection, and model validation. Significant findings indicate the following: (1) The models trained using several machine learning methods to assess the suitability of primary schools in nine cities for their surrounding facilities all obtained accuracy rates surpassing 72%, with Random Forest displaying the most consistent performance across several cities. (2) Developed cities prioritize spatial coordination among schools, with the SHAP feature representing 20.37% of its significance; conversely, less developed cities exhibit a stronger inclination towards coordinated placement with educational and training facilities, where the SHAP feature constitutes 20.52% of its significance. (3) The compatibility of primary schools with surrounding facilities suggests that Guangzhou, Zhengzhou, and Chongqing possess considerable prospective need for education, whereas the existing distribution in Beijing, Shanghai, and Tianjin is rather well-structured. This study provides a novel, data-driven framework for optimizing educational resource allocation, offering critical insights for achieving sustainable urban development and quality education in China’s cities as they evolve.