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Global shipping flows through strategically important restricted waters such as Panama and Suez Canals, and hence accidents occurring in the Canals will cause serious disruptions to global supply chains. In this paper, a new data-driven Bayesian network (BN) based risk model is developed to investigate how risk factors jointly generate impact on different types of maritime accidents within restricted waters. A new risk database involving 25 factors has been developed by manual analysis of all the recorded accidents from 2005 to 2021 that occurred in the world's important restricted waters including key maritime canals, channels and straits. A data-driven BN model is constructed to analyse the key risk influential factors (RIFs) contributing to such accidents. The model is verified by sensitivity analysis and real accident cases. Further, it is tested by the already known information of the 2021 Suez Canal blockage case to generate useful insights and draw the lessons to learn. In a retrospective analysis using the currently available limited information on the Suez Canal case, the implication of the case study shows a plausible explanation for the observed findings by scenario analysis. The findings can be applied to backward risk cause diagnosis for accident investigation and forward risk prediction for accident prevention in restricted waters to avoid the reoccurrence of similar accident to the Suze Canal blockage. • A new data-driven risk model enables the systematic analysis of the risk factors of maritime accidents in restricted waters. • Analysis of the dependencies between RIFs for maritime accidents in restricted waters. • Forward risk prediction for accident analysis and prevention. • Backward risk diagnosis for root cause analysis of maritime accidents. • Analysis of the Suez Canal blockage case study to provide insights for accident investigation and prevention. [ABSTRACT FROM AUTHOR]