As the data imbalance of pipeline working conditions decreases the accuracy of the pipeline leakage diagnosis, a method of pipeline leak detection and location based on imbalance data was proposed. First, the imbalance data of different working conditions were processed by K-means clustering algorithm and under-sampling to achieve the balance data. Then, the Fischer-Burmeister function was introduced into the learning process of the twin support vector machine (TWSVM), in order to avoid the matrix inversion calculation, and the balance data were input into the improved TWSVM to distinguish the pipeline leakage. Leak location was obtained by the cross-correlation function method. Moreover, a flow model of pipeline was put forward based on the Flowmaster software, and the proposed method was used to identify pipeline leakage. The experimental results show that the proposed method is more effective than the classical TWSVM and the Lagrange TWSVM to identify the pipeline leakage aperture and location.