This research examines the effectiveness of sophisticated statistical and machine learning techniques in forecasting corporate financial distress in India's hospitality industry, with a focus on the detection of distressed firms—companies that survive based on chronic financial underperformance. Stepping away from traditional overdependence on profitability ratios, the study combines a double-layered system with conventional finance indicators and operational working capital measures. Based on financial distress theory and empirical modeling, the research measures the predictive efficiency of logistic regression and ensemble modeling methods, placing emphasis on liquidity-focused variables demonstrating better discriminant performance compared to fixed asset-derived ratios. Conceptual model identifies the importance of operational efficiency, especially in the area of inventory turnover and suppliers' payment periods, as influential predictors of distress. Comparative output analysis demonstrates that models that utilize granular working capital metrics are more sensitive to initial distress signals than models based on capital structure or net profitability alone. This research challenges the shortcomings of traditional financial models in service-oriented settings and introduces a domain-specific analytical framework relevant to sustainable financial governance. The results have substantive consequences for institutional lenders, policy designers, and strategic managers who want to implement early warning systems and reduce systemic vulnerability in a post-pandemic economic setting.