In financial decision-making, predicting credit risk is a critical issue that requires machine learning models with high precision, robustness, and interpretability while handling structured tabular data. In this study, three different datasets—the Credit Risk, Australian Credit, and German Credit datasets—are used to compare advanced machine learning algorithms, such as LightGBM, Deep Neural Networks (DNN), TabNet, and a proposed Stacked Model. Each data set offers an in-depth evaluation of the model's performance in actual financial situations, with different levels of complexity and class imbalance.Results show that LightGBM and the Stacked Model frequently outperform other methods when evaluated on accuracy, generalization ability, and resilience to class imbalance. By finding a balance between interpretability and performance, the Stacked Model—which combines LightGBM, DNN, and TabNet with a meta-learner—displays exceptional predictive potential, making it a perfect option for evaluating credit risk. The difficulties of integrating deep learning with tabular financial data are demonstrated by the differing results of DNN and TabNet, especially in smaller datasets. Our findings demonstrate that ensemble learning improves prediction accuracy and stability in financial applications, emphasizing the importance of model selection based on dataset features. In order to improve model scalability and adaptability in complex financial contexts, future research will concentrate on real-time risk assessment, multi-class categorization, and transfer learning techniques.