This survey paper investigates the application of AI and machine learning techniques for the early diagnosis and detection of liver disease. Traditional methods of liver disease diagnosis, such as blood tests and imaging techniques, can be time-consuming and maybe there will be occurrence of human error. AI-based approaches offer the potential to accuracy improvement, efficiency, and accessibility of diagnosis. The research investigates a range of AI and ML algorithms, like decision trees, SVM, neural networks, random forests, and DL models. These algorithms are applied to analyze large datasets containing patient information and medical test results. The results of the models are evaluated using metrics like F1-score, precision, accuracy, recall, and AUC. The findings demonstrate the productiveness of AI-based methods in accurately detecting liver disease. Compared to conventional methods, AI models can provide more reliable and timely diagnoses, leading to improved patient outcomes. The research highlights the prospective of AI to transform the field of liver disease management and improve global healthcare.