Abstract. The rising floodwaters of the Maritza River, originating in the Balkans, affect those living in adjacent areas. The lower regions of the Maritza River are especially vulnerable to floods. The city of Edirne and the surrounding region in Turkey located downstream of the Maritza River are frequently affected by flooding. The section of the river inside the Turkish border is short; therefore, there is not adequate warning time for Turkey to alert the population against flash floods. For this reason, it is essential that Turkey acquire current flow information from Bulgarian sites for use in current flow prediction for Turkey's section of the river. To this aim, in order to predict the current flow of the Kirişhane station (Turkey) from the transboundary data of Plovdiv and Svilengrad stations (Bulgaria), four different models (M1‒M4) were developed by using the fuzzy inference system (FIS). Flow data from the Plovdiv, Svilengrad and Kirişhane stations were gauged every two hours covering the period from 9 February 2010 00:00:00 to 21 February 2010 22:00:00. In the first model, estimation was made using the current flows of the Plovdiv and Svilengrad stations. In the second model, estimation was made based on a two hour ahead prediction of the Svilengrad station and a four hour ahead prediction of the Plovdiv station. In the third model, calculations were based on predictions of four hours ahead of the Svilengrad station and eight hours ahead of the Plovdiv station. In the last model, estimation was based on predictions of six hours ahead of the Svilengrad station and twelve hours ahead of the Plovdiv station. The prediction ability of the FIS was evaluated by using the determination coefficient (R²), the Nach–Sutcliffe sufficiency score (NSSS), the normalized root mean square error (NRMSE), and the correlation coefficient (CORR). According to their performance criteria, all developed models produced highly satisfactory results. The M₂ model gave the best results according to both NSSS and R² values, whereas M₄ had the lowest NSSS and R² values. This study demonstrated that developed fuzzy rule-based models can satisfactorily predict flood waves and thus can be used for flood forecasting and warning systems.