The capital market of Saudi Arabia has experienced tremendous growth in Islamic finance tools like Sukuk under its economic development plan, named Saudi Vision 2030. It is essential for the capital market to evaluate the credit risk associated with Sukuk issuers for proper functioning. Generally, credit risk evaluation is performed using linear statistical and judgmental methods. However, it is difficult to evaluate credit risk using traditional methods because of the non-linear relationship between variables. In this study, a machine learning approach has been proposed for credit risk evaluation for Sukuk issuers. Using supervised machine learning techniques like Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, the study has evaluated various financial and macroeconomic variables associated with Sukuk issuers. The results indicate that the machine learning approach is much better for credit risk evaluation compared to traditional methods. The findings of this study will help implement Saudi Vision 2030 because it will highlight the use of artificial intelligence for credit risk evaluation in Islamic capital markets.