The comparison reveals that the primary source of data for existing studies is finite element simulation. Additionally, much of the data obtained through literature review also originates from FEM, as seen in the study by [PERSON] [27]. It was observed that studies directly utilizing borehole breakout size as an output are categorized into single and multiple outputs, and multiple output studies mainly employ artificial neural network (ANN) [28; 29; 30]. Single output refers to constructing multiple models based on the same input features to predict multiple targets separately when dealing with problems involving multiple prediction targets. On the other hand, multi-output refers to constructing a single model to predict multiple targets simultaneously. Compared to single-output approaches, multi-output models take into account the relationship between input features and targets, while also considering potential interdependencies among targets and objectives [31; 32]. Hence, when encountering multi-output problems with implicit relationships between outputs, multi-output models typically exhibit superior predictive performance compared to single-output models. Moreover, the training process of multi-output models tends to be more resource-efficient [33; 34]. Existing studies lack theoretical research on the correlation between breakout angle and breakout depth. However, [PERSON]'s study demonstrates that incorporating breakout angle into input features significantly enhances the prediction accuracy of breakout depth. This indirectly suggests an implicit correlation between breakout angle and breakout depth [35]. Consequently, for predicting borehole breakout size, multi-output models hold considerable promise. In existing multi-output studies on borehole breakout, single data sources are typically utilized, and certain crucial factors are often overlooked, resulting in limited generalization performance of the obtained models. Furthermore, no researcher has conducted a comparison between the established multi-output model and the single-output model, thus precluding the ability to ascertain the effectiveness of applying the multi-output structure. Obviously, further development and testing are still needed to validate the applicability of the multi-output structure in borehole breakout size prediction and to obtain models with high generalization performance and prediction accuracy for borehole breakout size prediction. In this study, the prediction of borehole breakout size is treated as a multi-output problem considering the influence of multiple features. Through literature research, nine features deemed relevant to borehole breakout were selected as inputs to the model, and a dataset comprising 501 sets of related numerical simulation and physical experiment data was collected. To accurately map the potential connections between the input features and the target, a novel meta-heuristic optimization algorithm (WAOA) combined with three base models (ANN, RF, and XGBoost) was chosen to construct a hybrid multi-output model. The trained WAOA-ANN, WAOA-RF, and WAOA-XGBoost models were utilized to predict borehole breakout size under various conditions. Subsequently, the three hybrid multi-output models obtained are compared and discussed alongside the single-output model within the same setup to validate the effectiveness of employing the multi-output structure and to determine the optimal approach for borehole breakout size assessment. Finally, the relationship between the input features and outputs of the optimal models is analyzed and discussed using the SHAP sensitivity analysis method.