Within the Healthy Cities and Sustainable Development Goals (SDGs) agendas, socioeconomic data are fundamental for tracking regional development. China, however, lacks a complete, long-term subnational socioeconomic dataset due to severe spatiotemporal missingness in official statistical yearbooks. We compiled 35 official socioeconomic indicators for 366 Chinese cities from 2000 to 2021, incorporated remote-sensing-derived covariates as auxiliary information, and applied a Bayesian spatiotemporal interacting varying intercepts (BSTIVI) model to capture the target variables’ spatial, temporal, and coupled spatiotemporal dependence. Model performance was evaluated using global Bayesian criteria and cross-validation, while local error distributions and temporal trends were visualized to examine imputation outcomes. Based on the completed dataset, we further derived a composite development index using entropy weighting and assessed spatial inequality with the Gini coefficient, coefficient of variation and hotspot analysis. The results show that BSTIVI achieved markedly better fit than traditional multiple linear regression (MLR). In cross-validation, 32 of 35 indicators achieved R<sup>2</sup> >= 0.95, RMSE and MAE remained low. The resulting data product showed strong imputation performance in both spatial and temporal dimensions. Analyses of the completed dataset revealed marked spatial inequality and clustering in urban socioeconomic development across China during 2000–2021. We ultimately produced the first long-term city-level socioeconomic dataset for China, comprising 35 indicators and one composite index, with Bayesian credible intervals for imputed values. This study provides both a new city-level data resource for China and a transferable framework for imputing missing subnational socioeconomic data worldwide, thereby supporting Earth system research and SDG implementation.