Amid the escalating demand for alternatives to petroleum resources and the imperative to decrease carbon emissions, there is an increasing interest in transforming lignocellulosic biomass into valuable chemicals. This study utilized machine learning models to analyze the acid-catalyzed liquefaction process of lignocellulosic biomass and to predict and characterize the yield and hydroxyl value (HV) of bio-polyols. A total of 612 yield data samples and 229 HV data samples were collected and analyzed. From these data, four machine learning models were constructed: Random Forest (RF), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). The Bayesian Optimization Algorithm (BOA) was applied to refine the hyperparameters of the models, thereby enhancing their predictive accuracy. To clarify the effects of input features on predictive outcomes and to understand their interactive dynamics, one-dimensional and two-dimensional Accumulated Local Effects (ALE) analyses were performed. The results show that the GBR model was particularly precise in forecasting the yield and HV of bio-polyols, with corresponding test set determination coefficients (R2) of 0.82 and 0.91. The most influential factors on yield were reaction time (15.1 %), lignin content (13.1 %), and glycerol mass (12 %), while for HV, they were glycerol mass (28.9 %), particle size (15.1 %), and liquid-to-solid ratio (11.1 %). The ALE analysis also exposed the intricate interaction mechanisms between feedstock composition and liquefaction conditions. The verification results confirmed that the optimal model displayed exceptional generalization capabilities, with a Mean Absolute Percentage Error (MAPE) of 9.18 %. Utilizing this model, a user-friendly software package has been developed to enable rapid and precise prediction of the lignocellulosic biomass conversion process. This research not only delivers strategies to cut down on experimental costs and time but also offers a novel perspective on the industrial utilization of biomass-derived polyols.