The proliferation of surface waste in water bodies poses significant environmental and ecological challenges. Traditional methods of waste detection are often labor-intensive and limited in scope. This paper presents a novel approach to surface waste detection using artificial intelligence (AI) and advanced imaging technologies. Leveraging cutting-edge techniques such as deep learning algorithms, high- resolution satellite imagery, and real-time data processing, our system offers an automated solution for identifying and monitoring waste in water bodies. We developed a robust AI model trained on diverse datasets, including satellite and drone-captured images, to detect various types of surface waste with high accuracy. The system integrates real-time processing capabilities to provide timely alerts and actionable insights for environmental management. Evaluation results demonstrate that our approach significantly improves detection accuracy and operational efficiency compared to conventional methods. This research contributes to the advancement of smart environmental monitoring systems and offers a scalable solution for mitigating the impact of surface waste on aquatic ecosystems.