We have employed Support Vector Regression, Random Forest Regression, and Gradient Boosting Regression to predict the Poisson's ratio of non-auxetic materials using supervised learning. The models were trained on the previously mentioned descriptors using 80% percent of the set, while 20 % of the set was reserved for testing. The coefficients of determination(R 2 ) and Root Mean squared Error(RMSE) were used to validate the models. The formulas for the same are given below.