Hydraulic fracturing serves as a critical in-situ stress testing technique, where the accurate determination of rock fracture pressure and closure pressure in fracturing intervals is essential for precise in-situ stress estimation. During hydraulic fracturing stress measurement, parameters including injection rate, viscosity, density, and compressibility ratio of fracturing fluid significantly affect the measurement accuracy of fracture and closure pressures, potentially introducing substantial errors in in-situ stress calculations. This study develops an MLP-KFold-based correction model for rock mechanical measurements by establishing a dataset derived from laboratory hydraulic fracturing simulations, incorporating fracturing fluid density, viscosity, injection rate, and corresponding rock fracture/closure pressures. Evaluation results demonstrate that the MLP-KFold model achieves superior performance with a coefficient of determination (R&sup2;=0.9937) on test sets, outperforming Random Forest (&Delta;+1.89 %), Support Vector Regression (&Delta;+4.05 %), and BiLSTM (&Delta;+5.34 %). Key error metrics including MAE (0.518), MSE (0.646), and maximum error (1.945 MPa) remain at minimal levels. The model exhibits enhanced data utilization efficiency and evaluation stability with small-scale datasets while effectively preventing overfitting and improving generalization capabilities. Field applications in in-situ stress measurements demonstrate significant reduction in average percentage differences of calculated stresses under different fracturing fluids (<em>&sigma;</em><em>_H</em>:<em></em><em>-</em>21.48 %,<em>&sigma;</em><em>_h</em>:-29.03 %), confirming its superior compensation effects. This research establishes a reliable compensation model for hydraulic fracturing pressures, providing an effective technical approach for correcting field measurement data and compensating in-situ stress calculation results, thereby contributing to the accurate assessment of regional stress profile states.