## 1 Introduction  
River pollution poses severe ecological threats globally [1, 2, 3], with inadequate scientific monitoring, especially in data-scarce areas like Iraq's Shatt al-Arab waterway. This diverse habitat faces dangers from wastewater, oil spills, and debris, causing water quality crises with significant health and environmental impacts, as documented in [4, 5]. Despite local vulnerabilities, advanced vigilance is lacking. This study employs machine learning for the aerial identification of pollutants in the Shatt al-Arab River, utilizing drone surveys to automate multi-class pollution detection. The models, trained on an extensive image dataset collected by DJI Mavic Air 2 and Mini 3 4k drones, facilitate the timely detection of oil spills, debris, and sewage. The selected algorithms enable comparisons with previous studies and demonstrate the proposed model's ability for real-time monitoring and pollution detection, addressing the longstanding challenge of environmental data scarcity in Iraq. This pioneering work introduces the first machine learning implementation for the pollution monitoring of Iraqi rivers, surpassing recent publications [6, 7, 8] with a simple, robust system achieving outstanding multi-class accuracy outcomes. In summary, this research pioneers drone-based water quality monitoring using machine learning in Iraq, advancing scientific understanding and supporting evidence-based decision-making for the protection of the imperiled river ecosystem, with the goal of transitioning models toward operational deployment and broader adoption. In recent years, river pollution has become a critical issue, particularly in Iraq, with the Bhatt al-Arab River facing challenges from industrial waste, sewage discharges, ship oil spills, and trash buildup. This pollution has significantly compromised water quality, posing threats to ecosystems, aquatic life, and public health. Reports from the Ministry of Environment in 2009 reveal that most Iraqi governorates do not meet national drinking water standards and WHO guidelines, with bacteriological contamination averaging 16%, ranging from 2.5% to 30% [9]. To address this crisis, developing an automated pollution detection system through machine learning is crucial. By utilizing advanced image classification algorithms trained on remote sensing and live feed data, the system can identify and monitor oil spills, trash, and wastewater in real-time [10; 11; 12]. Deployment along the Bhatt al-Arab River would enable swift responses to pollution incidents, preventing major damage. This innovation has the potential to revolutionize river conservation efforts in Iraq, restoring the historic waterways. By implementing such a system, proactive measures can be taken to reduce pollutants entering rivers, as water discharge releases harmful contaminants with the potential to cause diseases and adverse effects. Urgent and coordinated efforts are globally essential to combat various forms of pollution threatening rivers and to safeguard the environment, human health, and aquatic life [13]. The paper contributes to the field by addressing pollution detection in the Bhatt al-Arab River, utilizing advanced image processing techniques integrated with machine learning algorithms. Leveraging drone technology, the study collects over 1000 images to train models to detect oil spills, wastewater, and debris. The results showcase high accuracy rates, with Random Forest achieving 94% for oil spills, 92% for wastewater, and 95% for debris. Support Vector Machines and K-Nearest Neighbors also perform well, albeit with slightly lower accuracy. The findings prove the proposed system's potential as an efficient tool for monitoring pollution in the river, offering valuable insights for environmental management and conservation efforts in the region. The paper is organized into several sections, each addressing different aspects of the research process.