<![CDATA[Corporate financial distress prediction has shifted from classical ratio based statistical models toward data driven machine learning systems, raising concerns regarding the trade-off between predictive accuracy and interpretability. This study evaluates the integration of classical bankruptcy models with contemporary machine learning approaches to develop a robust and transparent early warning framework. Using a Literature Review, peer reviewed studies indexed in Scopus, Web of Science, and IEEE Xplore were synthesized, focusing on comparisons between the Z score model developed by Edward Altman, logistic regression, and modern algorithms such as support vector machines, ensemble learning, and neural networks. The findings indicate that machine learning models, particularly ensemble methods, demonstrate superior predictive capability in capturing nonlinear financial relationships. However, traditional accounting indicators remain fundamental predictors of distress. The study concludes that a hybrid framework integrating accounting based theory with machine learning optimization offers the most effective and strategically sustainable approach to corporate risk assessment.]]>
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