Liver cirrhosis is the most common cause of death among gastrointestinal diseases, with a global burden of 1.5 billion persons being affected annually. It is the 4^th and 10^th most common cause of disease in males and females, respectively. Acute-on-chronic liver failure (ACLF) development is the most common cause of death in these patients. The mortality of this dynamic syndrome ranges from 15-100% within 30-days and 90-100% within one year of presentation. Multiple life-threatening events like sepsis, organ failures, and gastrointestinal bleeding may occur during its course. This proposal is aimed to develop, validate, and deploy a novel model to predict mortality among patients with acute-on-chronic liver failure (ACLF). The model will be derived from clinical, biochemical, radiological and plasma proteomics data obtained from patients with ACLF. Principles of AI, machine learning, and bioinformatics will be utilized to build such a model. The model aims to incorporate the systems approach to medicine with clinical sciences and information technology sector. The proposal includes multiple innovations. It will develop a multimedia processing engine (converting the existing data in a ready format for ML and AI modeling), development, and deployment of AI-derived model for real-time predictive analytics. The models’ deployment will contribute to the development of user-friendly data analytic apps. The models may be used to assess the futility of care and allocate significant resources like ICU and mechanical ventilators to deserving patients. Importantly, patients’ lives can be saved with a timely decision for definitive treatment (liver transplant) using these models.