Reducing uncertainty and imprecision in medical data is essential to improve the quality of diagnoses and treatments. This paper presents a statistical—fuzzy hybrid framework that integrates information–theoretic metrics and classical statistical indicators with fuzzy logic techniques to address these challenges. The framework combines geometric mean filtering—as a computational operator—with generalized compensation operators and evaluates uncertainty reduction using mutual information, clairvoyance value (VoC), and traditional error indicators to enhance data clarity and minimize uncertainty. Validation was performed using synthetic and real–world datasets, including electrocardiographic (ECG) signals and computed tomography (CT) scans. The results showed a significant decrease in the VoC metric, indicating improved data quality and reduced uncertainty. In the ECG recordings, the VoC reduction was particularly notable, improving signal quality for subsequent analysis. Comparison with previous studies demonstrates that the proposed framework offers substantial improvements in uncertainty reduction. Practical implications include more accurate diagnoses and the potential for personalized treatments. The limitations of the study include the need for greater validation in diverse clinical datasets. In conclusion, the hybrid framework effectively improves the quality of medical data and contributes to emerging frontiers in medical data analysis through the integration of statistical methods, fuzzy logic, and uncertainty metrics to support optimized clinical decision-making.