The liver is a crucial and big organ in the human body, impacts the digestion system. Due to Liver diseases (LDs), so many deaths are occurred in worldwide that nearly 2 million deaths per year. The main LD complications are cirrhosis that 11th position in universal deaths, and others hepatocellular carcinoma and viral hepatitis that 16th leading position for global deaths. Fortunately, 3.5% of deaths are occurred due to LD. The capability of an ML approach for controlling LD can be identified through their factors, co-factors as well as complications respectively. In this research, we gather the personal and clinical information about1460 individuals with 17 LD feature attributes include diagnosis class attribute from 2018 to 2020 with good questionnaire from north coastal districts of A.P., India hospitals, and reputed clinical centers. We apply machine learning (ML) models like Logistic Regression (LR), SVM with RBF kernel, Naive Bayes (NB), KNN, and Decision Tree (DT or Tree). As per the ML model’s analysis, the DT model presents the superior classification accuracy that value is 0.9712 (97.12%) than other experimental ML models for the collected LD dataset. Our proposal model incremental hidden layer (HL) neurons ANN (Artificial Neural Network) solutes LD detection with the highest classification and testing accuracy that the value is 0.999 (99.9%) at the 30 HL neurons.