A huge demand for global trade has resulted in substantial growth of maritime traffic in the oceans. As a result, the failure risk of subsea pipelines due to third-party damage near shipping lanes has also considerably increased over time. Several factors that are typically considered in the risk assessment are dropped objects, dragged anchors, sunken ships and jack-up misposition. However, the industrial risk assessment codes still use semi-qualitative approach to analyse the risk, since relevant information is not complete in most of the cases. Given the possibility of a large number of time-variant uncertainties that will be involved in the analysis, Bayesian network approach can be a prospective tool to minimise and overcome the lack of data problem in the risk analysis. It is a reliable technique using well-established theoretical foundations of probability as the base for performing inference to determine the conditional probability distribution. This paper focusses on developing a dynamic Bayesian reliability model for subsea pipeline failure risk estimation analysis due to third party damage. Several types of floaters and structures will be introduced to the model. The proposed reliability assessment technique will be validated by comparing the estimated risk level result with a practical industrial code-based assessment result. It is expected that this technique will provide a reliable option for subsea pipeline reliability assessment due to third party damage.