The assessment of credit risk in the real estate supply chain is an essential part of financial risk management that influences investment decisions, financial stability, and the health of the overall real estate segment. Traditional financial credit risk models have long been used for the assessment of borrower credibility and potential default prediction with historical financial data, credit score, and some various financial ratios, while other methods could complement this approach. Although these conventional approaches have some merit, they frequently fail in capturing real-time market fluctuations, new emerging risks, and complex interdependencies that build creditworthiness. The introduction of artificial intelligence (AI) and machine-learning technologies has planted the seeds of change in the credit risk analysis horizon. AI-based models have given way to advanced analytical techniques that use big data, predictive analytics, and real-time insights to assess risk dynamically and more accurately.