Long-term pressure and flow rate history are important for reservoir characterization and reservoir management. However, a complete set of these data are often not available due to numerous technical difficulties. Currently, datasets with missing information are omitted and not considered for further analysis. In this study, we use machine learning algorithm via linear regression for flow rate history reconstruction. Only few studies have demonstrated the application of linear regression for well testing purposes. However, pressure and flow rate data in a producing field are comparatively longer and more complex. A combination of feature extraction and linear regression was applied for long term flow rate history reconstruction. The dataset used to evaluate the performance of the proposed method was obtained from a real producing field. This study indicated the high performance of linear regression at estimating missing flow rate history using available pressure readings in the dataset. Although linear regression has the benefits of high interpretability and fast computation time, it fails to perform well in reconstructing flow rate history when there is a significant degree of variation in the flow rate and pressure data.