An ecosystem approach to fisheries management is being developed around the world in an attempt to consider components of marine ecosystems other than just the exploited stocks. While numerous scientific studies on the consequences of fishing on marine ecosystems exist, most of their findings are too uncertain and fail to quantify the magnitude of the effects they describe to be used reliably in an environmental management and marine policy-making realm. To circumvent such a knowledge gap, we built and fitted a Bayesian network (BN), informed by a review of past studies, which integrates the links and direction of effects between socio-ecosystem components. These effects are represented as conditional probabilities, so that missing magnitudes of some ecosystem effects emerge from the numerous past cases that were reviewed. The Bayesian network is informed by a collection of cases extracted from 246 published scientific studies investigating relationships in marine ecosystems. We find that marine ecosystems are likely to be on a declining course under conjugated pressures of both fishing and changes to environmental conditions, e.g. due to ongoing climate change. By querying the fitted BN to obtain posterior probabilities under different scenarios, we showed that increasing fisheries regulation and environmental governance could partly mitigate these effects and decrease the risk of biodiversity loss, decreased profit and social inequity; the three pillars of the EU Common Fishery Policy (CFP). We discuss these findings with regard to particular fisheries across EU Waters, specifically: the North Sea, the Baltic Sea, the North Western and the South Western Waters. Furthermore, we discuss how fishing impacts interact with other ecosystem effects and pressures, caused by, or causing possible far-reaching consequences in marine ecosystem dynamics. The Bayesian network has some limitations regarding the ability to handle feedback loops that can occur in natural marine ecosystems. Nevertheless, such an approach helps to take a holistic view and integrate existing knowledge and new findings from future work, in a coherent, probabilistic risk assessment framework, while identifying what leverage and management actions may help nudge the system toward desired states.