Purpose: This study aims to predict financial distress in transportation and logistics companies before, during, and after the Covid-19 pandemic.

Research Methodology: A total of 23 transportation and logistics companies were selected as the research sample using purposive sampling method. Secondary data from audited financial statements for 2019–2023 were analyzed. Three financial ratios—Current Ratio (CR), Return on Assets (ROA), and Debt-to-Asset Ratio (DAR)—were used as input variables. An Artificial Neural Network (ANN) with a multilayer perceptron backpropagation algorithm was employed to train and test the prediction models. The best architecture was identified by comparing the model performance across variations in hidden neurons.

Results: The results reveal that companies reported as financially distressed have lower average values for the three ratios than companies not experiencing financial distress, making them suitable input variables. The best artificial neural network architecture in this study included an input layer with 60 neurons, a hidden layer with 15 neurons, and an output layer with a single neuron. This architecture achieved a training performance mean square error (MSE) of 0.125004 and an R-value of 50.00%. The study's findings suggest that 12 companies are likely to experience financial distress.

Conclusions: Financial ratios are effective indicators of distress, and ANN models can predict potential bankruptcy with a reasonable accuracy.

Limitations: This study is limited to three financial ratios and a single sector, which may not fully capture the broader determinants of financial distress.

Contribution: This study contributes to the financial distress prediction literature by applying ANN to transportation and logistics firms in Indonesia and offers practical tools for stakeholders to anticipate risks and design preventive strategies.