This study explores the connection between polycystic ovary syndrome (PCOS) and cancer risk using a dual-method approach that combines metabolic parameter analysis with image-based feature extraction and clustering. By applying fuzzy logic and deep learning techniques, the research establishes a reliable framework for predicting cancer risk in PCOS patients, highlighting notable correlations between the two conditions. The fuzzy logic system classifies cancer risk into four categories—no risk, low risk, medium risk, and high risk-based on factors such as age, BMI, and endometrial thickness. The findings indicate that postmenopausal patients with a high BMI and increased endometrial thickness have the greatest risk, whereas pre-menopausal patients with a low BMI and thin endometrium are at no risk. Image-based analysis complements this by examining structural similarities between PCOS and cancer patients. Using deep learning for feature extraction. The study identifies distinct or overlapping groupings between PCOS and cancer patients. High silhouette scores indicate well-separated clusters, while low scores suggest overlapping features, implying a potential association between PCOS and cancer. The findings highlight the critical role of combining metabolic and image-based analyses for assessing cancer risk in PCOS patients and emphasize the need for further exploration of the underlying mechanisms linking these conditions.