The liver is a vital organ responsible for numerous physiological functions in the human body. In recent years, the prevalence of liver diseases has risen significantly worldwide, mainly due to unhealthy lifestyle choices and excessive alcohol use. This illness is made worse by several hepatotoxic reasons. Obesity is the root cause of chronic liver disease. Obesity, undiagnosed viral hepatitis infections, alcohol consumption, increased risk of hemoptysis or hematemesis, renal or hepatic failure, jaundice, hepatic encephalopathy, and many other conditions can all contribute to chronic liver disease. Using machine learning for illness identification, hepatitis, an infection inflating liver tissue, has been thoroughly investigated. Numerous models are employed to diagnose illnesses, but limited research focuses on the connections between hepatitis symptoms. This research intends to examine chronic liver disease through machine learning predictions. It assesses the efficacy of multiple algorithms, including logistic regression, random forest, support vector machine (SVM), K-nearest neighbors (K-NN), and decision tree, by quantifying their accuracy, precision, recall, and F1 score. Experiments were performed on the dataset utilizing these classifiers to evaluate their efficacy. The findings demonstrate that the random forest method attains the highest accuracy at 87.76%, surpassing other models in disease prediction. It also demonstrates superiority in precision, memory, and F1 score. Consequently, the study concludes that the random forest model is the most effective for predicting liver disease in its early stages.