This study was a continuation of the our previous study in which the Remazol Yellow (RR) removal with S. cerevisiae was examined in the batch system and Artificial Neural Network (ANN) approach has been applied in 2 different stages to the biosorption process with the data obtained from this experimental system. In the first part, pH, initial dye concentration and initial biosorbent concentration are defined as system input data and the % dye removal value in the output layer is estimated with the artificial neural network. ANN training was conducted with the Levenberg-Marquardt feed forward algorithm and the experimental data were divided into 60% training, 20% validation and 20% testing. The maximum epoch value is determined as 12000 iterations. The system R2 value was set at 98% for training, 96% for validation and 98% for the entire biosorption system. In the second stage of the study, the effects of biosorption system parameters (pH, initial dye and biosorbent concentrations and temperature ) on the biosorption process were modeled by ANN. Experimental data is divided into 50% training, 25% validation and 25% test in this section. At the end of this study experimental and model estimates % dye removal values were compared and it was seen that the system is well modelled with ANN and the model has a good estimation ability.