The proposed data-driven multiphase virtual flow meter (DD-MVFM) blends data-driven ensemble machine learning with historical portable test reports. It provides precise real-time estimates of oil, gas, and water flow rates, along with production predictions. A key novelty is its efficient use of existing hardware for wellhead measurements such as temperature and pressure. The DD-MVFM can serve in three distinct ways: as a verification tool for multiphase physical flow meters (MPFMs), ensuring their accuracy; as a redundant system when MPFMs are unavailable or undergoing maintenance; and as a standalone replacement for MPFMs, significantly reducing operating costs and eliminating the need for extensive infrastructure setup. This innovation contributes to the crucial objective of reducing CO₂ emissions. The development of the DD-MVFM involves the fusion of data wrangling and machine learning algorithms for optimal performance. Initial testing indicates an 85% success rate, with potential for further improvement as more field test data are incorporated, making it a pioneering solution in the field of oil and gas management.