 Polycystic ovary syndrome, COS, is a complex disorder characterized by high levels of male sex hormones, irregular menstrual cycles, difficulty conceiving, and the presence of small cysts on the ovaries. Artificial intelligence AI has revolutionized healthcare, making remarkable contributions to science and engineering domains. In this paper, we demonstrate an AI approach using heterogeneous machine learning, ML, and deep learning, DL, classifiers, to predict pros among fertile patients. We used an open source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1 score of 98%, 97%, 98%, and 98%. Explanatory AI, XAI, techniques make model predictions understandable, interpretable, and trustworthy. To this end, we have utilized XAI techniques such as SHAP, Shapley Additive Values, LIME, local interpretable model explainer, ELI-5, CLADIS, and feature importance with random forest for explaining tree. This article was authored by Virata Vivekana, Krishna Raj Chatalga, Niranjana Sampathila, and others. We are article.tv, links in the description below.