The igneous rocks in Laizhou Bay, Southern Bohai Sea, exhibited complex and variable lithologies, posing significant challenges to accurately identify lithology using conventional logging cross-plots. To improve the precision of igneous rock lithology identification in study block A, a high-efficiency Light Gradient Boosting Machine (LightGBM) model was employed to identify lithology. Furthermore, the utilization of a greater number of hyperparameters by LightGBM necessitated the employment of the Whale Optimization Algorithm (WOA), which was renowned for its robust global optimization capabilities, to identify the optimal parameter solution. Consequently, a logging lithology identification approach was proposed based on WOA-LightGBM. Firstly, logging response of lithology was analyzed, and logging data with complete geological information, such as core and thin section, and complete regular nine logging curves were selected as the sample set. The sample set is then input into six models, namely, WOA-LightGBM, WOA-AdaBoost, WOA-SVM, LightGBM, AdaBoost, and SVM, for identification. And the results of identification process were compared and verified. Finally, the recognition models were applied to 15 wells. The results demonstrated that WOA-LightGBM model with optimal hyperparameters exhibited the highest recognition accuracy and the most robust generalization ability when the whale population was 50. There cognition accuracy in the sample set reached 91.62%, and macro-average F1-score was 87.41%, ROC-AUC was 0.9676, PR-AUC was 0.8726, Matthews Correlation Coefficient was 0.8902, and 0.3401 for Cross-entropy Loss. Thus, the WOA-LightGBM method can be employed as an effective means of intelligently recognizing the lithology of the igneous rocks in the Bohai Sea by utilizing logging curves. This approach can also serve as a reference for igneous lithology identification in other similar blocks.