The liver is the most fundamental organ in humans and is responsible for many bodily processes, such as protein and carbohydrate metabolism, enzyme activation, glycogen storage, and vitamin and mineral absorption. Almost 1 million people in India have liver illnesses, which are primarily underdiagnosed and have resulted in a number of fatalities. This project aids in the early detection of liver disorders, reducing the number of deaths and preventing India from becoming the "Global capital of liver diseases" by 2025. The sharp rise in obesity and unhealthy lifestyle rates ultimately reflects the propensity and frequency of liver-related disorders. The patient data sets are studied in this study to determine if it is likely that the subject will develop liver disease, which is predicted using a variety of machine learning methods such as Naïve Bayes, Logistic Regression, MLP (Multi-Layer Perceptron) and Random Forest. The most precise machine learning algorithm was used to forecast the outcome. The method employs the most precise model that has been trained to determine whether or not a person is at risk for liver disease.