The liver disease remains a medical condition without a specific allopathic cure at present. Detecting liver disease at an early stage can significantly improve survival rates. The prevalence of fatty liver disease is also on the rise in India, underscoring the need for a user-friendly method through which individuals can input parameters to determine their liver health status. To address this need, we propose the implementation of a Graphical User Interface. This interface would enable users to input various parameter values, thereby allowing them to ascertain whether they are affected by liver disease. Our study involved a thorough evaluation of several machine learning algorithms, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, LightGBM, RandomForest, and Extra Tree Classifier. Remarkably, our efforts culminated in an accuracy rate of 92% achieved by the Extra Tree Classifier. This achievement holds promising implications for accurate liver disease prediction and early intervention.