Sunflower, the most important oil crop plant in Turkey, In recent years, has decreased in production, and as a result of the increase in consumption, it has come to be imported in recent years. Especially in terms of satisfying the need for oils, it is essential to calculate the need so that it can be imported accordingly. In this study, ANFIS, a machine learning method, has been employed to estimate the quantity of production of sunflower. To increase the learning capacity of ANFIS, the input variables' membership functions have been specified via K-means clustering. The estimated production has been calculated in accord with the cultivated area, humidity, temperature and the duration of insolation and the amount of rainfall. This model has been applied to Edirne and the estimation has been reached with a mean squared error of 0,003243778. Thus, it is to be possible to estimate the amount of production in following years depending on the changing amounts of inputs.