In this study, a hybrid fuzzy intelligent method for diagnosing and managing uncertainty in input features to identify breast tumors in mammography images has been proposed. Moreover, a hybrid fuzzy evolutionary model has been applied to increase the system efficiency and optimize results. Soft computing models were used to detect the type of breast mass based on the analysis of features in mammography images. The combined models proposed in this study include FuzzyTBO and Fuzzy-PSO-TLBO models. The performance evaluation was conducted using the Receiver Operator Characterization (ROC) analysis in terms of accuracy and area under the ROC curve. A 10-fold cross-validation technique was conducted to divide the data into training and testing sections. The obtained results reveal an accuracy of 96. 27% for determining different types of mass based on tumors’ ؛ features according to the images. The presented model competes and outperforms other proposed models in previous studies. The outcome of this study may be promising for apropos diagnosis and effective treatments.