Oil flow rate testing is a crucial concept in oil fields where several methods facilitate well rate testing and measurement. Hundreds of multiphase flow meters (MPFMs) have been utilized to enhance the accuracy of testing measurement and provide reliable data for all fields. Even though these meters are of paramount importance, they require frequent preventive maintenance, calibration and manpower. In this paper, an Artificial Neural Network (ANN) model is developed as a backup tool to replace MPFM measurements when the device becomes defective or inoperable. Several correlations have been established to facilitate oil well testing at minimal cost; relying on surface production parameters to allow enumerating the oil flow rate without installing expensive equipment. An ANN model was developed using real-time wellhead parameters measured from equipment installed at the surface to designate properties and characteristics per reservoir. The ANN model was calibrated, tested and validated to achieve the most accurate results. The model was further optimized to attain a reliable tool for real-time rate estimation. In this paper, assessment of various correlations was conducted to compare the accuracy of each ANN-related empirical equation in five different datasets. The assessment also covers a wide range of data. More than thousands of data points from MPFM were compared to Towailib, Marhoun and Gilbert correlations, and showed highly deviated values with an average relative error of more than 40%. The same sets of data were tested using the newly developed optimized ANN model. The results from the model resulted in an average relative error of 3.7% compared with the MPFM rate measurements. Therefore, the new ANN model presented in this paper shows highly accurate results. The developed model contributed to enhancing testing efficiency and optimizing production. Indeed, utilizing this model is an essential practice for production engineers to validate well tests if prompt outcomes are desired and another reliable tool to estimate real-time rate production when metering device is down.