Ovarian illnesses, such as polycystic ovarian syndrome (PCOS) and ovarian cancer, have become more common in recent years and are a significant health concern for women all over the world—the need for improved diagnostic methods to detect these diseases early and accurately is more crucial than ever. Techniques in machine learning (ML) and artificial intelligence (AI) have become potential methods to improve the diagnosis of ovarian illness. This review paper presents a thorough overview and critical analysis of current developments and difficulties using AI and ML approaches to detect ovarian cancer. It was observed that the AI/ML-based algorithms have a high potential in detecting ovarian cancer.