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Liver diseases are one of the major health challenges in the world, and it is important to note that early diagnosis and proper diagnosis are key to good treatment and better patient outcomes. The recent developments in deep learning have transformed the field of medical diagnosis and presented some devastating powers to predict liver disease automatically. This is a review article that offers a critical study of deep learning methods in the prediction of liver disease. The article talks about data sets that are typically utilized, preprocessing, feature extraction, and performance measures. We also examine the difficulties encountered in applying deep learning models as predictors of liver diseases, such as data disproportion, interpretability, and clinical validation. The purpose of this review is to give a researcher and practitioner an idea of the state of the art methods and the future research direction in this vital area of healthcare.