Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O3 pollution. The results indicate that O3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R2 = 0.9043). Feature importance identifies NO2 as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O3 pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O3 transport with local emissions and meteorological conditions further increase the O3 pollution level. This study highlights the need to strengthen coordinated NOX and VOCs emission reductions and enhance regional joint prevention and control strategies in China.