ater is an indispensable source of life for all living things. However, these water resources (rivers) also carry sediment. These sediments cause limitations in the effective use of water, especially by increasing the amount of fill and water level in the dams. Nowadays global warming is widely seen and it is necessary to make regular measurements at rivers and lakes regularly in order to be used more effectively of water and its sustainability. These measurements are sometimes not systematically performed due to excessive labor and time requirements, and as a last resort, missing data are estimated or modeled to completion. One of the most widely used methods in recent years in many areas is Artificial Neural Networks (ANN). For this purpose, this study is based on predicting sediment yields by using ANN technique on E21A015 flow observation station (AGI) data and E21A015 sediment monitoring station (SGI) data in the Euphrates-Dicle basin and operated by State Hydraulic Works (DSI). In addition to these data, necessary climatic data will be obtained from meteorological observation stations (MGI) 17265 (Adıyaman) near the SGI stations. MATLAB software is used for ANN estimation. The results were compared with the multiple regression model.