Abstract: Ozone (O₃) is currently the primary pollutant affecting air quality in urban areas of China. Henan Province, serving as a crucial national transportation hub and center for people flow, logistics flow, and information flow, necessitates continuous improvement in O₃ forecasting accuracy. In order to further improve the forecasting accuracy of ozone pollution in multiple cities across Henan Province and optimize the forecasting duration and effectiveness, we used pollutant data and 6-hour Global Forecast System (GFS) meteorological field data during 2017-2021 to train an O₃ forecast model for 9 cities in Henan Province based on the machine learning method, and the model was applied to forecast O₃ concentrations in summer (June and July) of 2023. The forecasting duration of the model can reach 7 d, and the forecasting performance of the model was also evaluated quantitatively in this study. The results indicate: (1) The 7 d iteration model has good forecasting performance, with a correlation coefficient (r) between predicted and observed values reaching up to 0.91. And the values of r for the maximum daily 8-hour average ozone concentration (O₃ MDA8) forecasted for Zhengzhou City in 1-3 d lead time are 0.85, 0.84 and 0.84, respectively, which demonstrates a superior forecasting performance compared to existing studies. (2) The model performance in forecasting ozone varied across different cities. It performed better in the two boundary cities of Henan Province, Xinyang and Sanmenxia, with the root mean square error (RMSE) of the O₃ MDA8 in 6 d lead time remaining at about 27 μg/m³, and the mean relative bias (MRB) staying around 18%. However, the model performance is poorer for Anyang City, where summer O₃ concentrations are significantly affected by external provincial transport and southerly winds. (3) The model´s forecast performance was better for a lead time of 1-3 d, but deteriorated for a lead time of 4-7 d. Moreover, its forecast performance for 1 h average concentration of O₃ at 14:00 (averaged over 13:00-14:00) was worse than that for O₃ MDA8. (4) For O₃ MDA8 concentrations in the ranges of 0-100, 100-160, 160-215, and 215-265 μg/m³, the values of MRB for the 1 d lead time forecast are 21.65%, 13.63%, 9.63% and 12.81%, respectively, indicating a high accuracy. The results indicate that the machine learning forecasting method based on simultaneous iteration of major atmospheric pollutants can provide an accurate and timely 7 d O₃ forecast for various cities in Henan Province, which is suitable for regional ozone pollution event prediction and early warning system. For cities with poorer forecast performance, it is expected that future improvements in model accuracy can be achieved by incorporating additional O₃ precursor data, using meteorological forecast data with higher spatiotemporal resolution, and more comprehensively integrating meteorological features.