Endometriosis, a prevalent yet challenging condition to diagnose, affects a significant number of women worldwide, often leading to delayed treatment and management. Traditional diagnostic methods, while effective, have limitations in terms of accuracy and invasiveness. Recent advancements in artificial intelligence and medical imaging technologies offer new possibilities for improving the diagnosis of this complex condition. The study synthesizes findings from various research works that have explored the individual and combined capabilities of magnetic resonance imaging and ultrasound enhanced by artificial intelligence, machine learning, and deep learning techniques, for the automatic detection of endometriosis. It particularly focuses on the enhanced diagnostic accuracy, sensitivity, and specificity achieved through the integration of artificial intelligence AI algorithms. The paper examines the results of fusion imaging, where the complementary nature of magnetic resonance and ultrasound is leveraged, providing a more comprehensive diagnostic tool. The findings suggest that their synergistic use leads to improved diagnostic accuracy and efficiency. This integrated approach not only aids in early detection and better characterization of endometriosis but also provides valuable insights for effective treatment planning. The paper concludes with a discussion on the potential of this combined approach in revolutionizing the diagnosis and management of endometriosis, highlighting future directions and implications for clinical practice.