Simulation models are essential tools in the maritime industry for predicting ship behavior and interactions in changing marine environments. This report explores the importance of these models and outlines how they might improve ship operations, design, and safety. It highlights the need for ongoing updates to guarantee their dependability. The performance of vessels must be improved while emissions and expenses are reduced since maritime transportation is essential for global trade. Simulation models provide useful insights for this project. This thesis provides an intelligent way for automating testing of marine simulation models in the Open Simulation Platform (OSP) environment by utilizing deep learning techniques. With the help of artificial intelligence and computer algorithms, this methodology aims to increase testing accuracy while lowering costs. Customized tests for simulation models are developed using generative adversarial networks (GANs), which mimic real-world behaviors. Although the computational requirements present difficulties, the accuracy and dependability shown by GANs surpass those of traditional techniques. This thesis emphasizes the usefulness of deep learning in maritime software testing, improving performance evaluation through careful investigation. Extending the variety of scenarios and improving the test creation procedures are two future directions. In the end, this project helps the marine industry advance toward improved effectiveness, environmental awareness, and safety. The conclusions drawn from the paper highlight the good potential of cutting-edge technology in creating a future for maritime transportation that is sustainable.