In this study, an ANN-based model was developed to predict nitrate concentrations in drainage waters based on parameters that are easier and cheaper to measure in an irrigation area within the Lower Seyhan Basin, one of Turkey's important agricultural production regions. For this purpose, daily water samples were collected from a drainage measurement station during the 2022 and 2023 water years, and nitrate concentrations were determined in the laboratory. In addition to nitrate concentrations, other parameters, such as flow rate, EC, pH, and precipitation, were also measured simultaneously. The complex relationship between measured nitrate values and other parameters, which are easier and less costly to measure, was used in two different scenarios during the training phase of the ANN-Nitrate model. After the model was trained, nitrate values were estimated for the two scenarios using only the other parameters. In Scenario I, random values from the dataset were predicted, while in Scenario II, predictions were made as a time series, and model results were compared with measured values for both scenarios. The proposed model can be confidently used to fill gaps in the dataset (Scenario I) as well as to predict nitrate values in a time series (Scenario II).