Pipeline monitoring provides operators with invaluable information regarding the potential risks that may threaten the integrity of the entire line. Pipeline leakage results in severe environmental and financial costs that can be avoided through leak monitoring systems. This study introduces a novel approach to estimate leak flow rate with vibration analysis in above-ground liquid pipelines. Vibrations of the pipeline are acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. It is achieved by developing a leak-induced vibration (LIV) model that simulates the pipe dynamics through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design the Kalman filter. The Kalman filter transfer function predicts the leak forces and estimates the fluid release through correlation analysis of the leak forces and leak flow rate experimentally. The proposed methodology requires the information of the leak source and its location in developing the LIV model. Thus, to facilitate the proposed leak flow rate estimation system, this study also introduces leak detection and localization. An artificial intelligence (AI)-based leak detection algorithm is developed to minimize the leak interpretation errors. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage by applying the pressure gradient intersection method. Finally, a lab-scale experimental setup is manufactured to verify the dynamic LIV model and test the proposed methodology. The performance of the proposed method shows high accuracy for leak detection, localization, and leak flow rate estimation, respectively.