An incomplete data set of flow rate and pressure is detrimental to reservoir management and operation. It has the potential to increase uncertainty and has the potential to unfavorably affect operational and managerial decisions. Such a data set might transpire because of failure in the flowmeters, pressure gauges, and/or unrecorded shut-in periods. This study proposes and evaluates unified physics and data-based analytics for “learning” the underlying behavior of a reservoir and reconstructing missing gas, oil, and water flow rates. The proposed workflow is evaluated using real field data obtained from a North Sea reservoir. Validation is done by using a whiteness test, a goodness-of-fit test, and a novel physics-based validation using material balance and pressure back-calculation. The outcome has shown the capability and flexibility of the selected machine-learning techniques in estimating the missing flow rate on the basis of pressure responses. The features that are extracted and expanded on the basis of physics have resulted in a high-fidelity model with less computation time.