With the growing complexity of enterprise financial data, traditional financial warning models face limitations in handling large datasets and outliers. This study proposes a novel financial warning model integrating ensemble learning and stacked generalization. A two-layer fusion model is constructed using stacking generalization, while SMOTETomek addresses data imbalance. Model parameters are optimized via grid search and five-fold cross validation. Experimental results demonstrate superior performance, with an average accuracy of over 90%. The accuracy on the training and testing sets reach 0.93 and 0.95, respectively. The model achieves a low false positive rate (3.8%) and false negative rate (3.2%) in the low debt category, outperforming comparison models. It also exhibits high resource efficiency and low time costs, making it an ideal tool for enterprise financial early warning. The model aids in identifying financial risks, enabling proactive response strategies, promoting healthy financial management, and enhancing market stability.