A problem of interest to aircraft maintainers is automatic detection, classification, and prognosis of potential critical component failures in the gas turbine engines. Automatic monitoring offers the promise of substantially reducing the cost of repair and replacement of defective parts and may even result in saving lives. In this paper, an integration architecture is proposed for gas turbine engine prognosis through utilization of dual particle filters and ensemble learning methods. In our proposed framework, dual particle filters are utilized to estimate the states as well as the health parameters of the gas turbine engine system. An Long-short term memory neural network based observation forecasting scheme is developed to extend the system observation profiles to future time horizon. Moreover, we develop multiple predictors, each with differing properties so that multiple estimates can be made based on different possible scenarios. A data fusion architecture is presented that combines multiple prognostic techniques weighted by their individual prediction uncertainty for the remaining useful life (RUL) of a gas turbine engine asset. The architecture is based on the concept of ensemble learning method. To verify and validate the above results and as a case study, our proposed ensemble approach is applied to predict the health condition of a dual spool jet engine. The developed ensemble architecture provides improved decision support in comparison to the existing single prognostic technique methods that are available in the literature.