This investigation seeks to reconcile the interpretability of artificial intelligence with the imperative of maintaining optimal performance, through the introduction of the IFNN model. This model represents a sophisticated iteration of the Fuzzy Neural Network (FNN) paradigm, engineered for enhanced interpretability. At the heart of this innovation lies the application of the Adam optimization algorithm, integrated into a tri-layered architectural construct. This approach is deliberately designed to elevate the models' accuracy across an array of datasets, thereby positioning the IFNN model as an example of an interpretable AI system that does not compromise on precision. A seminal aspect of this model is its capacity to transmute logical neurons within the intermediary layer into clear fuzzy rules. This transformative process propels the model beyond the confines of traditional AI frameworks, ushering in a new era of transparency in the AI decision-making arena. Such progress is achieved through a meticulous fuzzy rule-based examination, anchored by an exhaustive appraisal of interpretability metrics, including but not limited to sensitivity, completeness, and the analysis of fuzzy rule consequents. These logical neurons, the progenitors of the aforementioned fuzzy rules, endow the model with the ability to engage in deep, interpretable analyses of data. The IFNN model has been tested through statistical analysis, interpretability assessments, and empirical validation against real-world datasets about sepsis identification, showcasing its unparalleled ability to unlock and articulate the complex knowledge embedded within data. This model represents a significant evolution in AI methodologies, providing a clear window into the rationale underpinning its decisions, achieved through an advanced fuzzy rule-based methodology and a full spectrum of interpretability metrics.