A gearbox oil state recognition method based on PCA feature optimization and AdaBoost ensemble learning is proposed to address the problems of low accuracy and limited generalization ability in traditional gearbox oil analysis methods. Firstly, the multi parameter oil data is cleaned using box plots and SMOTE interpolation to improve the quality of the oil data; Secondly, PCA is used for oil product feature optimization to obtain a subset of oil product feature optimization that is helpful for identification. While effectively integrating multi parameter information of oil, it can significantly reduce the time complexity of model operation; Then, a basic model for oil state recognition is established using BP neural network, and the GWO wolf pack optimization algorithm is introduced to optimize the model. A weak classifier GWO-BP with optimal initial weights and thresholds is constructed, and an adaptive boosting AdaBoost algorithm is adopted to combine multiple weak classifiers GWO-BP, integrating them into a strong classifier with strong robustness. Finally, the experimental data was applied for verification and analysis. The experimental results showed that the proposed method had the best performance, with an average recognition rate of 99.30 ± 0.16% and an average time of 32.77 ± 1.27 seconds. It could quickly, efficiently, and accurately identify the oil state of the gearbox lubricating oil, laying a good foundation for realizing online oil state recognition of gearboxes and having important engineering application value.