This paper presents a comprehensive study on utilizing artificial intelligence (AI) and advanced detection techniques for the study and monitoring of ships. The primary objective is to prevent various issues, such as ship intrusion detection, ship detection in satellite images, and ship detection in river images. To achieve this, the study proposes innovative methods; including enhancing the capabilities of YOLOv3 and YOLOv8 neural networks to improve the accuracy of ship detection. Additionally, the study leverages IoT technology for real-time tracking and integrates feature fusion modules for more effective information integration. A crucial aspect highlighted in this study is the necessity of controlling pollution caused by ships. By addressing this environmental concern, the study aims to contribute to the preservation of marine ecosystems and enhance maritime safety. The results of the study demonstrate a significant enhancement in detection accuracy, showcasing the potential of these advanced methods for efficient and reliable ship monitoring systems.