This study introduces a two-stage approach for high-resolution leak localization in large-scale pipelines by coupling machine learning to transient hydraulics. The method includes two stages of leak zone identification and in-zone Leak detection. A transient simulation model using the Method of Characteristics (MOC) is developed to generate the learning data for the pipeline under consideration. Afterward, the problem search space is reduced, and the maximum leak detection error is restricted by determining the most likely leaky zone using Support Vector Regression (SVR). Then, the zone dataset is provided by introducing leak candidates to the identified zone. After that, an ensemble classifier consisting of a set of linear discriminant components is trained to reliably detect the exact location of the leak using the majority voting technique. The models are applied to a theoretical pipeline and an experimental Reservoir-Pipe-Valve (RPV) system. The performance of the applied machine learning algorithms is compared to well-known algorithms considering a variety of kernels and hyperparameters. The impacts of different levels of uncertainty in pipe roughness and initial flow on the models' accuracy are also investigated. The results manifest that the proposed model has high accuracy and is stable, and robust against the hydraulic simulation uncertainties.