In this paper we consider a class of recently introduced jump-Markov switching models, involving a hidden process X, an observed process Y and a latent process R which models the switches or changes of regimes in (X,Y). We address the Bayesian prediction problem, and we show that the p-step ahead a posteriori conditional expectation (and associated conditional covariance matrix) can be computed exactly linearly in time.