aspg-41-3484-1792222935 20261017074215 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Metaheuristic Optimization Review MOR 3066-280X 2025 3 1 Aya Ebrahim Department of Applied Health Sciences, Higher Technological Institute of Applied Health Sciences, Mansoura, Egypt Asmaa H. Rabie Department of Computers Engineering and Control Systems, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt El-Sayed M. El-Kenawy Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt Hossam El-Din Moustafa Professor at the Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Egypt Today, new Artificial Intelligence (AI) techniques are utilized to help doctors forecast the occurrence of diseases because of the necessity of sustaining public health and early disease diagnosis. One significant kind of liver damage is liver cirrhosis, which typically results from long-term liver damage brought on by a variety of liver conditions and diseases, including hepatitis, persistent alcoholism, or heredity. We created this review to provide an overview of liver cirrhosis since it is essential to identify it early and prevent the damage from spreading throughout the liver tissues. In order to identify liver cirrhosis from biomedical markers rather than images, this study has recently conducted nine studies overlaying it with various artificial intelligence deep learning techniques. Our suggested approach used various Machine Learning (ML) models to predict the signs of cirrhosis in conjunction with other illnesses. Because this condition is so important, it is important to summarize these studies based on the methodology and findings of detection accuracy and precision. 2025 01 04 3484 https://creativecommons.org/licenses/by/4.0/ 10.54216/MOR.030101 https://americaspg.com/journal/41/article/3484 https://americaspg.com/storage/articles/manuscripts/01KXBQNRTXYE74139GH2C3VMKR.pdf