Ship detection is one of the major applications of maritime surveillance, environmental monitoring, and naval operations, detected from satellite images by traditional methods. Because of the varying weather conditions, occlusions, and open ocean’s vastness, it tends to be labor-intensive. Deep learning models have proven to be a valuable tool in overcoming these challenges through improved accuracy and efficiency in ship detection in high-resolution satellite images. This work proposes a ship detection system using the YOLOv8 architecture; however, here the specific choice of the algorithm architecture is made based on real-time capabilities, and, naturally, there is room for achieving a good balance between the speed of detection and performance. The system applied demonstrated high accuracy in the detection of sea vessels—97% accuracy, which would suggest that YOLOv8 could in fact be used successfully for rapid, reliable maritime monitoring. The core strength of the system relies on feature extraction in YOLOv8, which makes it capable to detect and classify ships while separating them from other non-ship objects like sea clutter or debris, so it has good capabilities to maintain reliable real-time identification in all scenarios considered. Results show that this system can thereby noticeably improve maritime security through the exact and timely provision of information on the presence of ships. Such improvements are invaluable to coast guards, naval forces, and authorities responsible for maritime surveillance of the oceans as they may even reduce the human workload and improve situational awareness. Future endeavors shall be based on performance improvement of detection at adverse conditions, for example, sea states that are rough or bird occlusions, which cause false positives. These innovations aim to provide a scalable, all-in-one solution for wide-area maritime surveillance against present and future challenges in maritime security.