In this paper, an ensemble learning method, in the form of a Categorical boost (Catboost) algorithm is adopted as an effective predictive tool for envisaging values of average surface roughness and material removal rate during the CNC turning operation of C45 steel workpiece with a tungsten carbide cutting tool. In order to develop the related models, a grid with combinations of different hyperparameters is created and tested for all the possible hyperparametric combinations of the model. The configurations having the optimal values of the considered hyperparameters and yielding the lowest training error are finally employed for predicting the response values in the CNC turning process. The performance of the developed models is finally validated with the help of root mean squared percentage error. It can be observed that Catboost can be efficiently applied as a predictive tool with excellent accuracy in machining processes.