The goal of this study is to use hybrid deep learning models on the problem of corporate financial distress prediction to enhance the level of accuracy in bankruptcy prediction. The ongoing research work will apply three various hybrid neural network models, which include Convolutional Neural Network within Bidirectional Long Short-Term Memory Network (CNN-BiLSTM), Convolutional Neural Network within Long Short-Term Memory Network (CNN-LSTM), and Convolutional Neural Network within Gated Recurrent Unit (CNN-GRU), which are competent to find spatial and temporal pat kterning features from corporate financial information. The integrated models incorporated financial signals like debt-to-equity ratio, net profit margin, and return on assets and nonfi nanc information like market sentiments and credit ratings to create an overall model architecture to predict distress status. The datasets, which contain a mixture of financial ratios, time-series data, macroeconomic variables, and administration efficiency metrics, that have been cleaned and normalized to attain optimum training performance, are trained on the models. The output from testing the performance reveals that CNN-BiLSTM was the best model that attains, among others, an accuracy level of 97.87%, good precision, recall, and F1-score level. Its value of AUC-ROC was the best also, which amounted to 0.98, indicating its enhanced capability to distinguish between companies that are and those that are not distress companies. CNN-LSTM and CNN-GRU follow, with accuracies of 92.3% and 87.67%, respectively, which shows strong predictive capabilities but not as effective as the former. The present study contributes to the field of financial analytics through a reliable model of corporate bankruptcy prediction, yielding precious insights to stakeholders over mitigating financial risks and better decision making in real time applications.