Marine life is constantly at risk due to the intrusion of artificial materials introduced by human activities. These synthetic waste materials originate from various sources, including debris and trash released from boats, ships, and recreational vessels, pull outs from nearby beaches; oil discharge, and chemical runoff from factories, as well as improper waste disposal by surrounding restaurants and tourist hubs. Once in the water, these materials can pose severe threats to marine life, such as entanglement, ingestion, poisoning, and habitat destruction, thereby endangering marine ecosystems and biodiversity. Humans depend heavily on marine life for food, medicine, and plant-based resources. Resilient Artificial Intelligence plays a vital role in addressing marine pollution, particularly in detecting and removing artificial materials from underwater environments. Given the complexity and biodiversity of the ocean, direct removal of waste is not straightforward. To effectively train AI systems, vision-based algorithms must be developed with a focus on high visual accuracy for real-time underwater artificial material detection. Techniques like Sea-Thru use depth maps to remove the water distortion and enhance scene clarity, significantly improving machine vision. YOLOv8 is optimum for detecting artificial material.