The addition of graphene (Gr) to high-entropy alloys (HEAs) has recently been demonstrated to endow the alloys with increased strength and plasticity in experiments and simulations. However, determining the effects of various factors on the mechanical properties of Gr during the experimental process is difficult due to its special structure and the complexity of its components. Investigation of the mechanical properties (Young's modulus, yield strength and toughness) of HEA/Gr under different simulation conditions through experiments and simulations is not only costly in terms of raw materials but also time-consuming. This study aimed to address the aforementioned problem and investigate the mechanical properties of HEA/Gr under different conditions. First, the dataset necessary for the machine learning model was constructed using molecular dynamics simulation and downscaled from 59 to 21 features using principal component analysis. Then, five machine learning models, including XGBoost, light gradient boosting (LGBoost) machine, random forest, AdaBoost, and decision tree, were employed. Finally, 10-fold cross-validation and grid search were utilized during the training process to search for the best parametric model, and the coefficient of determination R2 and mean square error MSE were used as metrics. Results indicate the superiority of the XGBoost and LGBoost models to other models in predicting HEA/Gr mechanical properties. Among the three properties studied, the models predicting Young’s modulus and toughness revealed an R2 value above 85%. Two models realized an R2 greater than 90% for Young’s modulus, while all three models achieved an R2 greater than 85% for toughness.