Investors and financial analysts increasingly rely on accurate tools to evaluate and interpret financial statements in order to make informed decisions and minimize risks. Among various methods, financial ratios remain one of the most widely used indicators for assessing corporate financial health and identifying early warning signs of potential crises. The main objective of this study is to predict the likelihood of corporate financial distress using liquidity ratios, with a comparative focus on two powerful machine learning techniques: support vector machines (SVM) and backpropagation neural networks (BPNN). The research employs a causal-comparative design and combines both quantitative and qualitative approaches, analyzing data from 2009 to 2013 (1389–1393 in the Iranian calendar). The findings reveal that the BPNN consistently outperforms the SVM, achieving higher predictive accuracy with statistically significant differences: 0.001 in year t, 0.005 in year t-1, and 0.030 in year t-2. These results demonstrate the neural network’s superior ability to forecast corporate bankruptcy up to two years in advance, offering a valuable tool for investors and stakeholders. Furthermore, the analysis highlights that the capital-to-total-assets ratio exerts the strongest influence on bankruptcy prediction. The study underscores the importance of integrating advanced computational methods with traditional financial indicators to enhance decision-making in dynamic economic environments.