The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 2016–2023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with “tanh” activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies.