The diagnostic process for diseases with a common onset of symptoms is inherently complex due to overlapping medical test results, which makes discrimination amongst conditions difficult. Such diagnostic ambiguities pose significant challenges for clinicians, particularly in catering for the most optimal treatment plan for a patient. This study proposes a Fuzzy Bayesian Network (FBN) based approach that addresses these limitations by utilizing a unique blend of fuzzy logic and Bayesian Networks. The fuzzy logic component is adept at handling vagueness in the form of linguistic variables, thereby enabling nuanced interpretations of ambiguous inputs. Bayesian Networks provide an interpretable probabilistic framework for modeling and analyzing the conditional dependencies between diagnostic factors. By combining these methodologies, the proposed system enables the prioritization of diseases from a list of possible diagnoses, even under limited data or resource constraints. Furthermore, it incorporates the opinions of multiple medical experts, thereby making the predictions more robust. The proposed FBN approach has been applied to solve a case study concerning Sepsis and Pneumonia to prove its relevance. The experimentation was successful in identifying the causative disease as Pneumonia based on the provided symptomatic probability scores with a confidence score of 0.85.