The Indian sugar industry has been experiencing chronic budgetary instability due to unreliable sugarcane harvests, unpredictable global sugar prices, delayed compensation to farmers, and frequent government intervention in pricing and subsidy plans. These dynamic uncertainties cannot be accurately reflected in traditional financial performance measures, which typically rely on fixed-ratio analysis and post-hoc data, thereby producing erroneous forecasts and diminishing the value of decision-making information to stakeholders. To fill this gap, the current study proposes an AI-based framework for financial sustainability and risk analytics that incorporates multi-source, real-time information, including commodity market trends, ethanol blending targets, climatic variations in sugarcane yields, and changes in policies issued by the Government of India. With high-end machine learning models and dynamic time-series prediction, the proposed solution not only assesses profitability, liquidity, and leverage ratios but also considers risk indicators, including price volatility, subsidy addiction, and climate-related disruptions, to provide predictive information. Financial and operational data of major Indian sugar firms from 2018 to 2024 were experimentally validated using live market and weather data, and the predictive performance of the AI-powered system was compared to standard financial analysis techniques. Findings indicate that the advanced framework is more accurate in forecasting financial risks, allows for predicting changes in policy-related profits much earlier, and provides a better decision-support system for investors, policymakers, and industry leaders. The paper concludes that incorporating real-time data streams into financial analytics will significantly enhance transparency, flexibility, and strategic planning within the Indian sugar industry, providing a template for other agro-based industries experiencing comparable volatility.