Well-performance investigation highly depends on the accurate estimation of its oil and gas flowrates. Testing separators and multiphase flowmeters (MPFMs) are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9265 SCF/STB, the oil rate varied from 1156 and 7982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using 30% of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared with 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percentage error (AAPE) of 1.3 and 0.7%, respectively, in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.