This study develops an integrated causal model of marine incidents using cluster analysis and causal inference techniques to identify and classify the major factors contributing to marine accidents. This approach allows systematic examination of the interdependencies between different factors, providing a holistic understanding of incident causation. Data collected from maritime safety reports and accident databases were analyzed to identify the major causes of incidents with a focus on the interactions between human error, technical failures, and environmental conditions. In addition to these strategies, the study proposes the adoption of data-driven risk assessment models that leverage real-time data from ship operations to predict potential failures and implement corrective actions before incidents occur. This proactive approach ensures that safety measures are continuously updated to reflect the latest operational realities. By combining the results of cluster analysis with a structured cause-and-effect model, this study provides a comprehensive framework for improving maritime safety, offering targeted recommendations for vessel operators and regulators. These findings contribute to the development of more robust safety management systems that are better suited to meet the complex challenges of modern maritime operations.