The spatial distribution of elderly care service resources is crucial to the overall efficacy of the national eldercare system. Utilizing ArcGIS to observe the distribution of elderly care service resources at the prefecture-level city scale across China, this paper finds that the distribution highly aligns with the demographic pattern of the “Hu Line”. It exhibits significant positive spatial autocorrelation and agglomeration characteristics, forming five high-density core belts. Addressing the current “spatial mismatch” between the distribution of elderly care service resources and the aging population, this paper first delineates the supply-demand matching relationship via spatial thermal analysis. Subsequently, it employs cross-sectional OLS regression coupled with city-level clustered robust standard errors to thoroughly investigate the underlying economic and social mechanisms driving spatial resource allocation. Empirical results indicate that first, population aging exerts a fundamental driving effect on resource agglomeration; however, this mechanism is highly contingent upon the “conditional triggering” of social security coverage and residents’ income levels. Second, this paper reveals a “cost disease” crowding-out effect within the eldercare industry in developed regions, where exorbitant land rents and operational costs prevent certain economic hubs from achieving a proportional expansion of basic care beds. Third, environmental amenities and the abundance of medical resources are emerging as core driving forces in reshaping the spatial network of elderly care services, while regional demand heterogeneity significantly impacts the qualitative structure of these resources. Based on the aforementioned mechanisms, this paper proposes targeted optimization countermeasures: first, to address the pain point of “medical-elderly care separation”, it explores dynamic conversion mechanisms between “medical beds” and “elderly care beds”, alongside seamless medical insurance settlements. Second, to counteract the “cost disease” in megacities, policies should promote the cross-regional portability of eldercare subsidies and establish “wellness enclaves” in peripheral node cities. Third, confronting the urban-rural spatial divide, financial transfer payments should implement a “spatial tilting” strategy to construct a three-tier “county-town-village” inclusive service network, thereby effectively bridging the “spatial depression” in rural elderly care services.