Particle filters are well-known as powerful tools for accomplishing state and parameter estimation and their propagation prediction in nonlinear dynamical systems. Their ability to include system model parameters as part of the system state vector is among one of the key factors for their use in prognostics. Estimation of system parameters along with the states produces an updated model that can be used for long-term prediction. This paper presents a novel method for uncertainty management in long-term prediction using particle filters. In our proposed approach, the observation prediction concept is applied in order to extend the system observation profiles (as time series) for future. Next, particles are propagated to future time instants according to the resampling algorithm instead of considering constant weights for their propagation in the prediction step. The uncertainty in the long-term prediction of system states and parameters are managed by utilizing fixed-lag dynamic linear models. The observation prediction is achieved along with an outer adjustment loop to change the observation injection window adaptively based on the Mahalanobis distance criteria. The proposed approach is applied to predict the health of a gas turbine system that is affected by the degradation in the system health parameters.