The corporate credit rating of a firm is one of the most important factors in whether or not it can pay back its debts. To make smart investing decisions and handle risks well, you need to be able to accurately predict these ratings. We think that knowing a company’s credit score early on helps banks and other financial institutions better predict how the market will develop in the future. We suggest a new stacked ensemble method in this paper that will make credit rating projections more accurate. We employed a dataset that had thirty attributes for each organization. Twenty-five of these were financial indicators that looked at cash flow, operational performance, debt levels, liquidity, and profitability. We did a full exploratory data analysis (EDA) initially so that we could understand the dataset and find its most essential aspects. This stage gave us important information about each company’s financial health by enabling us see important patterns in the financial indicators. We next trained and compared a number of machine learning models, including K-Nearest Neighbors, Random Forest, Support Vector Machine, Neural Network, Naive Bayes, XGBoost, Gradient Boosting, and Logistic Regression. We used accuracy and other important criteria to test these models. Our results reveal that the stacked ensemble method was more accurate than any of the individual models, including XGBoost and Gradient Boosting. We have included a large table with accuracy scores for each model to make these results easier to understand. These results highlight how effective ensemble learning is and how it can help analysts and financial professionals make decisions in the real world.