Static Poisson's ratio (νs) is an essential property used in petroleum calculations, namely fracture pressure (FP). The νs is often determined in the laboratory
however, due to time and cost constraints, quicker and cheaper alternatives are sought, such as data-driven models. However, existing methods lack the accuracy needed for critical applications, necessitating the need to explore more accurate methods. In addition, the previous studies used limited datasets and they do not show the relationships between the inputs and output. Therefore, this study developed a reliable model to predict the νs accurately using the nineteen most common learning methods. The proposed models were created based on a large data of 1691 datasets from different countries. The best-performing model of the nineteen models was selected and further enhanced using various approaches such as trend analysis to improve the model's performance and robustness as some models show high accuracy but show incorrect relationships between the inputs and output because the machine learning model only built based on the data and do not consider the physical behavior of the model. The proposed Gaussian process regression (GPR) model was also compared with published models. After the proposed GPR model was developed, the FP was determined based on the proposed GPR νs model and the previous νs models to evaluate their accuracy on the FP determinations. The best approach out of the published and proposed methods was GPR with a coefficient of determination (R2) and average-absolute-percentage-relative-error (AAPRE) of 0.95 and 2.73%. The GPR model showed proper trends for all inputs. The cross-plotting and group error analyses also confirmed that the proposed GPR approach had high precision and surpassed other methods within all practical ranges. The GPR model decreased the residual error of FP from 87% to 26%. It is believed that such a significant improvement in the accuracy of the GPR model will have a significant effect on realistic FP determination.