Credit risk remains a fundamental concern in corporate debt issuance, influencing investor sentiment, bond pricing, and regulatory oversight. Traditional models such as Altman’s Z-Score and structural approaches, though historically significant, fail to capture the complexity of today’s financial environment characterized by global volatility, heterogeneous data sources, and rapid corporate restructuring. With the advent of big data and advanced machine learning, there exists an opportunity to enhance predictive modeling frameworks. This study proposes a data-driven credit risk modeling framework designed specifically for corporate debt issuance. Using firm-level financial data, macroeconomic indicators, and market-based variables, we compare traditional statistical approaches with modern machine learning algorithms. Empirical findings from simulated datasets highlight that ensemble learning methods such as Gradient Boosting outperform conventional logistic regression in predictive accuracy, while Random Forests provide strong interpretability. Our results suggest that leveraging data-driven analytics can improve default risk estimation, thereby fostering better decision-making for issuers, investors, and regulators.