This research introduces a novel methodology for generating realistic ship encounter scenarios to test intelligent navigation systems. The approach combines historical AIS data, maritime traffic patterns, and regulatory frameworks to create diverse and challenging test cases. We develop algorithms that simulate various environmental conditions, vessel behaviors, and collision risk situations. The generated scenarios are validated against real-world encounters and expert assessments. Results demonstrate the effectiveness of our method in evaluating collision avoidance systems and supporting the development of autonomous vessel navigation technologies.