Rock geomechanical properties impact wellbore stability, drilling performance, estimation of in situ stresses, and design of hydraulic fracturing. One of these properties is Poisson’s ratio which is measured from lab testing or derived from well logs, the former is costly, time-consuming, and does not provide continuous information, and the latter may not be always available. An alternative prediction technique from drilling parameters in real time is proposed in this paper. The novel contribution of this approach is that the drilling data is always available and obtained from the first encounter with the well. These parameters are easily obtainable from drilling rig sensors such as rate of penetration (ROP), weight on bit (WOB), and torque. Three machine-learning methods were utilized: support vector machine (SVM), functional network (FN), and random forest (RF). Dataset (2905 data points) from one well were used to build the models, while a dataset from another well with 2912 data points was used to validate the constructed models. Both wells have diverse lithology consists of carbonate, shale, and sandstone. To ensure optimal accuracy, sensitivity and optimization tests on various parameters in each algorithm were performed. The three machine-learning tools provided good estimations; however, SVM and RF yielded close results, with correlation coefficients of 0.99 and the average absolute percentage error (AAPE) values were mostly less than 1%. While in FN the outcomes were less efficient with correlation coefficients of 0.92 and AAPE around 3.8%. Accordingly, the presented approach provides an effective tool for Poisson's ratio prediction on a real-time basis at no additional expense. In addition, the same approach could be used in other rock mechanical properties.