Arsenic contamination in shallow aquifers of Holocene alluvial basins is a serious health risk affecting millions of people [1]. Detection of arsenic hotspots is a slow and tedious process based on the analysis of groundwater samples. This study improves arsenic risk prediction by incorporating geomorphological features such as oxbow lakes and clay plugs into a machine learning (ML) approach. Advances in remote sensing [2], often combined with ML, enable the efficient detection of these and other proxy features, significantly reducing reliance on labour-intensive fieldwork. By combining these features with environmental and demographic data, the approach provides more accurate and cost-effective risk assessments, enabling better-targeted interventions in vulnerable regions and supporting proactive environmental monitoring.