Financial health serves as a vital measure of a company’s performance, especially for small and medium-sized enterprises (SMEs). Reliable predictions of financial health enable companies to make well-informed decisions and strengthen their competitiveness in the global marketplace. This paper introduces a financial knowledge representation model, termed the Finance-Knowledge Model, which structures key factors that influence the financial health of small businesses within a knowledge base integrated into an expert system. Building on this framework, a forecasting methodology is proposed by combining knowledge-based reasoning with machine learning techniques to improve interpretability. The experimental results demonstrate that the proposed approach outperforms existing methods in predicting financial distress. This hybrid model serves as an effective tool for early warning systems and improves financial decision-making for SMEs.