AI-Driven Impact Assessment of Stakeholder Value Creation and Long-Term Sustainability in Corporate Revivals under India’s IBC 2016

India The Insolvency and Bankruptcy Code (IBC), 2016 has brought much change to the corporate insolvency resolution system by focusing more on revival over liquidation on time. Although dozens of studies have investigated recovery rates and operational efficiency using the IBC, few empirical studies assess the value creation by the stakeholders and sustainability of rejuvenated firms over a long period of time through the lenses of more sophisticated analytical methods. This paper suggests an Artificial Intelligence (AI)-based impact assessment model to determine the post-revival performance of corporate entities that were resolved under the IBC. The machine learning models such as Random Forest (RF), XGBoost and Artificial Neural Networks (ANN) were used to analyze a multi-dimensional dataset consisting of financial indicators, recovery rates of stakeholders, variables of governance and post-resolution operations. The given model forecasts the long-term sustainability results with more than 90 accuracy and ANN proves better predictive performance (AUC>0.94). As per the empirical results, stakeholder recovery has been highly improved, financial stability is increased and operational efficiency is measured within three years of resolution. The soundness of the AI-driven framework is statistically validated with the help of paired t-tests, ANOVA, and model comparison methods. The findings indicate that the IBC has performed optimisation of stakeholder value that is measurable and the ability to turn around corporate sustainability in most of the situations that have been solved. The proposed structure will provide a data-driven and scalable policy evaluation instrument to regulators, financial institutions, and insolvency experts to determine the effectiveness of revival and the risk of future losses.