Over time, ovarian tumors have remained a prominent cause of mortality among women worldwide. Given the complex and uncertain origins of the disease, various sophisticated diagnostic models have emerged to differentiate between benign and malignant tumors. This present study aims to employ fuzzy logic for tumor characterization, focusing on the expression of apoptotic genes Bax, Bcl-2, and Cas3. Using MATLAB software, we devised a fuzzy system to classify ovarian tumors based on qualitative polymerase chain reaction (qPCR) data from eleven samples. The system outputs three potential diagnoses: benign, borderline, and malignant tumors, with assessment scores ranging from 0 to 1. Scores below 0.5 suggest benign tumors, scores above 0.5 indicate invasive potential, and those near 0.5 indicate borderline cases. The system consistently produced results aligned with known sample diagnoses upon inputting relative gene expression values. This method shows promise in advancing ovarian tumor characterization, potentially improving treatment outcomes. Nonetheless, further comprehensive research and an extensive literature review are essential to validate these initial findings.