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Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. This study examined the Huaihai Economic Zone (HEZ)—a quadri-provincial border area—by constructing the evaluation systems for new urbanization and urban resilience. The development indices of the two systems were measured using the entropy weight-CRITIC method. The spatiotemporal evolution characteristics of their coupling coordination degree (CCD) were analyzed through a CCD model, while key driving factors influencing the CCD were investigated using a grey relational analysis model. The results indicated that both the new urbanization construction and urban resilience development indices in the HEZ exhibited a steady upward trend during the study period, with the urban resilience development index surpassing the new urbanization construction index. The new urbanization index increased from 0.3026 (2013) to 0.4702 (2023), and the urban resilience index increased from 0.3520 (2013) to 0.6366 (2023). The CCD between new urbanization and urban resilience reached 0.7368 by 2023, with 80% of cities in the HEZ achieving good coordination types. The variation of the CCD among cities was minimal, revealing a spatially clustered coordinated development pattern. In terms of driving factors, economic development level, public service capacity, and municipal resilience level were identified as core drivers for enhancing coupling coordination. Infrastructure construction, digital capabilities, and spatial intensification served as important supports, while ecological governance capacity remained a weakness. This study establishes a transferable framework for the coordinated development of secondary economic cooperation region, though future research should integrate diverse data sources and expand indicator coverage for higher precision. Moreover, the use of linear models to analyze the key driving factors of the CCD has limitations. The incorporation of non-linear techniques can better elucidate the complex interactions among factors. Full article