<p>Liver Disease is the second major deadly disease that causes severe effects in human lives. In the decade of improved medical diagnosis, medical professionals utilize several pathological techniques to detect the disease, but with the evolved Artificial intelligence (AI), liver ailment diagnosis has achieved certain peaks. Various researchers were involved in raising the path of liver disease detection, and additionally, diverse sets of machine learning (ML), as well as Deep Learning (DL) algorithms, have been developed in the research area. In general, ML algorithms are utilized in the healthcare industry that work in conjunction with clinical data to perform efficient medical observations, while the DL mechanisms assist in enhancing clinical decision-making through actionable patient data analysis. The study intends to provide a comprehensive examination of various algorithms utilized in liver disease detection specifically in terms of the ML as well as DL algorithms that support them in potentially making new-decisions, also enabling prior identification of liver dysfunction. Thus, in the detection of liver disease, several evolved researches contributed the major part in providing accurate detection of liver disease along with certain advancements. The review addresses a wide range of subjects about the detection of liver disease, such as its theoretical underpinnings, industrial applications, benefits over conventional techniques, and so forth. The survey's results will help advance knowledge of the fundamentals influencing technology adoption and provide guidance for policies aimed at encouraging broad integration.</p>