Accurately predicting covenant breaches in private loan portfolios is essential for lenders, investors, and credit risk managers seeking to mitigate default risk and safeguard capital. Traditional ratio-based credit analysis using indicators such as debt-to-equity ratio, interest coverage, and current ratio has long been the foundation of financial monitoring. However, these static metrics often fail to capture nonlinear relationships, evolving borrower behavior, and hidden distress signals in privately held firms where financial transparency is limited. This paper provides a broad overview of covenant risk assessment and then narrows its focus to a comparative evaluation between machine learning-driven credit risk models and conventional ratio analysis. The study analyzes how machine learning methods including random forests, gradient boosting, survival analysis, and neural networks leverage transactional data, payment histories, macroeconomic trends, and alternative data to generate dynamic probability-of-breach forecasts. In contrast, traditional ratio-based assessments rely on periodic financial statements and predefined thresholds, making them less adaptable to real-time risk fluctuations. Additionally, the paper explores the role of feature engineering, explainable AI, and time-series modeling in improving interpretability and regulatory acceptance of machine learning models. Results indicate that machine learning approaches outperform traditional analysis in early detection of covenant breaches, especially in multi-lender syndicated loans and middle-market private credit portfolios. However, concerns remain over data availability, model transparency, and compliance with lending regulations. The paper concludes that hybrid approaches integrating ratio diagnostics with machine learning outputs offer the most reliable framework for covenant monitoring, enhancing predictive power while preserving interpretability for credit committees and regulators.