This article examines the issues of forecasting enterprises’ liquidity and solvency indicators based on artificialintelligence. The main objective of the study is to compare the effectiveness of traditional methods and artificial intelligencemodels in assessing financial stability. The article analyzes the possibilities of predicting the future state of liquidityindicators using neural networks. Particular attention is paid to identifying complex and nonlinear relationships in theforecasting process. The research results demonstrate that artificial intelligence models provide high accuracy in the earlyidentification of financial risks. The conclusions obtained have practical significance for improving enterprises’ financialmanagement systems. The scientific novelty of the article lies in the comprehensive application of artificial intelligenceapproaches to forecasting liquidity and solvency.