Fuzzy logic significantly contributes to model building and the decision-making process by addressing imprecision and uncertainty to achieve practical, robust, and viable solutions to real-world problems. In recent times, researchers have extensively employed the fuzzy logic approach to analyze various health events. This study aims to use fuzzy logic approach for developing a model to estimate the possibility of death caused by heart failure practically. Three factors are selected as input variables for the model: age, ejection fraction, and serum creatinine, with heart failure serving as the output variable. The model is trained and tested using medical records (available online) of 105 female heart failure patients out of which 70% of the data were utilized for training and another 30% for testing purpose. The Mamdani inference and Centroid method are used for inference and defuzzification of the estimated risk values respectively. The model’s performance is evaluated using ROC curve analysis, statistical measures, and individual observations. The results demonstrate that the model based on fuzzy logic-effectively estimates the risk of mortality by heart failure, and also highlights its potential application in studying heart failure with the selected variables.