Women today are most likely to be experiencing Polycystic Ovary Syndrome (PCOS), a hormonal imbalance disorder. This disorder mostly affects women’s ovaries, where a large number of tiny fluid-filled sacs called cysts—also referred to as follicles—form around the ovary’s periphery. The exact root cause of PCOS is still unknown despite advances in science. Using ultrasound (US) scans to identify numerous follicles is an efficient way to diagnose PCOS early and schedule treatment. The primary purpose of this article is to determine whether or not a woman has PCOS or not without supervision from a physician. In this work, we provide a deep learning (DL) method based on transfer learning for PCOS classification using US ovarian images, with the goal of improving diagnostic efficiency and precision. InceptionV3 and ResNet50 models, which had accuracy rates of 99.68% and 97.5%, respectively, were used for this research. The study’s findings show that, as compared to conventional machine learning (ML) techniques, transfer learning-based classification performs better in PCOS variant classification. This study aims to accurately diagnose PCOS in patients and use our proposed model to treat PCOS. Gynaecologists and other medical professionals can benefit from our model’s ability to provide a prompt, reliable, and correct response.