Degradation of subsea pipelines in the presence of corrosive agents and cyclic loads may lead to the failure of these structures. In order to improve their reliability, the deterioration process through pitting and corrosion-fatigue phenomena should be considered simultaneously for prognosis. This process starts with pitting nucleation, transits to fatigue damage and leads to fracture and is influenced by many factors such as material and process conditions, each incorporating a high level of uncertainty. This study proposes a novel probabilistic methodology for integrated modelling of pitting and corrosion-fatigue degradation processes of subsea pipelines. The entire process is modelled using a Dynamic Bayesian Network (DBN) methodology, representing its temporal nature and varying growth rates. The model also takes into account the factors influencing each stage of the process. To demonstrate its application, the methodology is applied to estimate the remaining useful life of high strength steel pipelines. This information along with Bayesian updating based on monitoring results can be adopted for the development of effective maintenance strategies.