Liver diseases are increasingly becoming one of the most fatal health conditions in several countries, especially after Covid-19 (i.e., after 2019) and the prevalence of liver disease has been rising since then due to factors such as excessive alcohol consumption, inhalation of harmful gases, and the intake of contaminated food, pickles, drugs and medications and not to miss, also due to the Covid-19 virus. To address this issue, several multimodal data are collected and given as input to build categorization and forecasting models aimed at predicting liver diseases, especially Hepatitis C and, by utilizing machine learning approaches, we comprehensively assess the patients' liver conditions and the stage of Hepatitis C. We first categorize the results into positive and negative outcomes using rudimentary machine learning algorithms. As we process the liver parameters and their percentages, we present the results as votes derived using the Unconventional Consensus Classifier Algorithm to classify the stages of Hepatitis C. This project aims to develop a robust machine-learning model for the categorization and forecasting of liver disease diagnosis. Leveraging various machine learning algorithms, including decision trees, support vector machines, and so on, the project focuses on accurately predicting liver disease based on a set of medical and demographic features. By analyzing the available existing data and utilizing advanced data preprocessing and feature engineering methods, the proposed system seeks to assist healthcare professionals in early diagnosis and treatment planning, ultimately improving patient outcomes.