eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-03-31 18 1 353 372 10.13005/bpj/3093 64578 article Kamal Upreti 1 Jossy George 1 Shitiz Upreti 2 Shubham Mahajan 1 Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, Uttar Pradesh, India Department of IT & Management, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India PCOS is an endocrine illness that affects 6–10% of women worldwide. It can cause a variety of symptoms, including irregular menstruation periods, ovarian cysts, and hyperandrogenism. Its lack of defined biomarkers, overlapping symptoms, and heterogeneity make diagnosis difficult. By studying hormone profiles, identifying patterns difficult to see with conventional approaches, and offering great precision and accuracy, AI and ML techniques are transforming diagnostic difficulties. Hybrid models in the list include SWISS-AdaBoost and ensemble learning techniques that have accuracies up to 98% enabling early diagnosis along with appropriate treatment strategies. Early detection by technologies such as AI will prevent significant health complications that are PCOS-related, such as infertility, type II diabetes, or cardiovascular diseases. This study depicts the transformative role of the application of AI in diagnosing cases of PCOS and highlights the possibility of facilitating clinical decision-making, reducing potential diagnostic delays, and enhancing improvements in patient outcomes. Future research should hence be directed towards the establishment of AI within healthcare with consideration of validation, reliability, and ethical considerations to maximize its use in clinical practice. https://biomedpharmajournal.org/vol18no1/polycystic-ovary-syndrome-diagnosis-the-promise-of-artificial-intelligence-for-improved-clinical-accuracy/ Artificial Intelligence Endocrine disorders Hyperandrogenism Infertility Polycystic ovaries Rotterdam Criteria