Traditional methods for evaluating soil conditioning effects in earth pressure balance (EPB) shield tunneling rely heavily on laboratory tests and independent index analysis. However, existing tunneling parameter prediction models often neglect conditioner parameters. To address these limitations, the study proposes a classification and prediction model for soil conditioning effects that integrates tunneling parameters with stratum and conditioner parameters. The data is obtained from 1 372 rings in the sandy strata of the EPB shield-bored section of the Shenyang metro line 3. Based on the acquired data, the following parameters are selected as evaluation indicators for soil conditioning effects: the field penetration index I_FP, torque penetration index I_TP, and penetration degree (S). Following data cleaning, outlier removal, and standardization, a four-level evaluation model (excellent, good, medium, and poor) is established using the K-means clustering algorithm. Additionally, random forest, extremely randomized trees (ET), and gradient boosting regression models are built by combining stratum and conditioner parameters to predict tunneling parameters. The results show that (1) K-means clustering effectively differentiates between levels of conditioning effects; (2) the ET model outperforms others in predicting tunneling parameters, achieving coefficient of determination (R²) values of 0.877 1, 0.881 2, and 0.843 4 for I_FP, I_TP, and S predictions, respectively; and (3) the overall accuracy of the classification-prediction model, validated on the test set, is 80.69%.