The accurate determination of the pressure, volume, and temperature (PVT) properties such as bubble-point pressure and oil formation volume factor is important in the primary and subsequent development of an oil field. These two parameters are essential for all petroleum engineering calculations such as reservoir simulations, recovery estimates, material balance calculations, well completion, facility design decisions, and production optimization strategies. In this study, a new approach is presented for predicting bubble-point pressure and oil formation volume factor for crude oil samples collected from different regions around the world. The regions include major oil-producing fields in North and South America, the North Sea, Southeast Asia, the Middle East, and Africa. The new approach, which is based on nonparametric optimal transformations, is called alternating conditional expectation (ACE). The transformations are totally data-driven and do not assume any a priori functional form. The data set used in the study consists of 5200 points that represent worldwide crude. An additional 200 PVT data sets were used to investigate the effectiveness of the new proposed method to predict outputs from inputs that were not used during the training process. The ACE model is able to predict the bubble-point pressure and oil formation volume factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. The excellent results obtained from the proposed model establish a new simple tool for calculation of the two properties. The accuracy of the models developed in this study was compared in detail with several published correlations.