The objective of this study is to predict corporate financial distress in an emerging economy like India, where detailed bankruptcy data of firms is limited. For this purpose, an extensive panel dataset obtained was utilized. The study makes a significant contribution by developing a novel framework to identify early signs of financial distress among Indian firms through a combination of firm-specific financial indicators. This approach not only increases the sample size of distressed firms but also improves the robustness of the results. The study applies both traditional Logistic Regression and Bayesian modeling techniques to predict financially distressed firms in the Indian corporate sector. Furthermore, a comparative analysis of the predictive performance of these two approaches was conducted. The results from both in-sample and out-of-sample evaluations consistently demonstrate the superior predictive performance of the Bayesian approach compared to the Logistic model. Overall, the study provides a valuable framework for identifying early warning signals of corporate failure in the Indian context, an area that remains relatively underexplored in existing literature.