This study employs an explainable machine learning (ML) framework to examine the regional dependencies of sur- face ozone biases and their underlying drivers in global chemical reanalysis. Surface ozone observations from the Tropospheric Ozone Assessment Report (TOAR) network and chemical reanalysis outputs from the multi-model multi-constituent chemical (MOMO-Chem) data assimilation (DA) system for the period 2005–2020 were utilized for ML training. A regression tree-based randomized ensemble ML approach successfully reproduced the spatiotemporal patterns of ozone bias in the chemical reanalysis relative to TOAR observations across North America, Europe, and East Asia. The global distributions of ozone bias predicted by ML revealed systematic patterns influenced by meteorological conditions, geographic features, anthropogenic activities, and biogenic emissions. The primary drivers identified include temperature, surface pressure, carbon monoxide (CO), formaldehyde (CH₂O), and nitrogen oxides (NO_x) reservoirs such as nitric acid (HNO₃) and peroxyacetyl nitrate (PAN). The ML framework provided a detailed quantification of the magnitude and variability of these drivers, delivering bias-corrected ozone estimates suitable for human health and environmental impact assessments. The findings provide valuable insights that can inform advancements in chemical transport modeling, DA, and observational system design, thereby improving surface ozone reanalysis. However, the complex interplay among numerous parameters highlights the need for rigorous validation of identified drivers against established scientific knowledge to attain a comprehensive understanding at the process level. Further advancements in ML interpretability are essential to achieve reliable, actionable outcomes and to lead to an improved reanalysis framework for more effectively mitigating air pollution and its impacts.