In a Wireless Sensor Networks (WSN) based fluid pipeline leak monitoring system, numerous sensors are deployed along the pipeline networks. A great amount of measurements are continuously transmitted from the sensor nodes to their corresponding sink nodes. The energy consumed on data transmission dominates the power depletion of a WSN system. To reduce the amount of data transmission and prolong the lifetime of WSN, in this paper, a Combined Dual-Prediction based Data Fusion (CDPDF) method is proposed. Transmissions are only triggered if the measurement is substantially different from the predicted value. Furthermore, unlike existing methods which establish the predictor by merely considering the measurements from a single sensor, the proposed CDPDF learns and updates the predictor by integrating measurements from multiple neighboring sensors, hence the spatial cross-correlation is taken into account and the prediction accuracy is significantly improved. In this paper, an Enhanced Leak Detection and Isolation (EnLDI) method is also proposed in which several important parameters, such as the friction factor and the pressure wave propagation speed, can be online updated, resulting in improvement of the leak localization accuracy. Experimental case studies are conducted. By employing the proposed CDPDF and EnLDI methods in pipeline networks monitoring, the accuracy of leak isolation is significantly increased with reduced data transmission demands. Note to Practitioners—This work delivers a hybrid scheme that combines machine learning based data fusion and transmission, with model-based leak detection and isolation. The work is motivated by the problem of high energy consumption on data transmission and poor leak diagnosis accuracy in WSN based pipeline networks monitoring system. To reduce the energy consumed during frequent transmissions among sensor nodes, in this paper, a machine learning based data fusion method is proposed which can eliminate most of the redundan...