This article presents a study on the use of machine learning to predict bond investment risks. The study focuses on using the Standard & Poor's (S&P) rating and regression models to predict bond investment risks. The results show that the random forest model achieved the highest accuracy of 80% in predicting bond investment risks. This study provides valuable insights into the use of machine learning to assist investors in making informed investment decisions. The results also suggest that machine learning could be an effective tool for predicting bond investment risks and could potentially enhance investment outcomes.