Reliable long-term city-scale nitrogen dioxide (NO
2) records are essential for evaluating urban air quality change, but the short observation period of TROPOMI limits long-term applications. This study developed a TROPOMI-referenced machine learning framework to reconstruct annual tropospheric NO
2 column density for 436 cities in China, Japan, South Korea, North Korea, and Mongolia from 2000 to 2022 using 18 annual predictors comprising five natural environmental variables, six meteorological variables, and seven sectoral anthropogenic NO
x emission variables. To reduce spatial leakage, the 436 city polygons were assigned to a regular 5° × 5° grid using the largest equal-area polygon intersection fraction. The 61 occupied, non-overlapping blocks were allocated deterministically to five folds, with all 2019–2022 observations from each city retained in its assigned block. Model performance was calculated from the concatenated predictions for the five held-out block sets. Among nine models, random forest (RF) achieved the best independent test performance (R
2 = 0.885; RMSE = 1.968 × 10
−5 mol m
−2). During 2005–2022, annual city-level RF–OMI correlations ranged from 0.850 to 0.945, and their normalized regional annual series were strongly correlated (r = 0.877). Against ground observations, RF better represented intercity differences in China (mean annual r = 0.791 versus 0.735 for OMI), whereas OMI performed better at the Japanese city scale (0.857 versus 0.810 for RF); nevertheless, RF closely reproduced the Japanese national annual trend (r = 0.989). The East Asian mean increased significantly during 2000–2011 (Theil–Sen slope = +0.095 × 10
−5 mol m
−2 yr
−1), declined significantly during 2011–2018 (−0.140 × 10
−5 mol m
−2 yr
−1), and remained nonsignificantly negative during 2018–2022 (−0.060 × 10
−5 mol m
−2 yr
−1). China peaked in 2011, Japan and North Korea showed significant long-term decreases, South Korea showed a significant overall decline, and Mongolia had no significant full-period trend. SHAP analysis showed that industrial combustion emissions, surface pressure, and road emissions had the highest global mean absolute SHAP values, with nonlinear and direction-dependent associations with RF predictions. The resulting dataset supports regional and national long-term NO
2 assessment, while country-specific and city-scale uncertainties should be considered in local applications.
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