Saudi Arabia's Vision 2030 has increased the strategic importance of diversified and sustainable financing for enterprises participating in infrastructure, energy, industrial, technology, tourism and service-sector expansion. At the same time, debt financing exposes firms to interconnected credit, liquidity, refinancing, interest-rate, cash-flow, project and concentration risks. Conventional ratio analysis and management forecasting remain essential, but periodic and predominantly historical approaches may not fully exploit the financial, operational and market data now generated by enterprise resource planning, treasury, banking and project-management systems. Artificial intelligence (AI), machine learning (ML), predictive analytics and explainable artificial intelligence (XAI) can extend established finance practice by identifying nonlinear risk patterns, improving early-warning capability and supporting forward-looking debt-capacity assessment. This paper develops an AI-enabled financial risk management framework for debt financing decisions in Saudi enterprises. It uses an integrative structured review of recent peer-reviewed literature on corporate default prediction, credit risk, financial distress, loss given default, explainable AI and financial risk analytics, combined with official Saudi sources on financial-sector and debt-market development. The resulting framework comprises six layers: integrated data, AI-supported risk identification, predictive debt-capacity analysis, scenario and stress testing, explainable decision intelligence, and governance with continuous monitoring. The framework treats AI as an augmentation of professional judgement rather than an autonomous financing authority. It proposes practical metrics, a governance workflow and implementation priorities for CFOs, FP&A teams, treasury functions, risk managers and boards. The study contributes by connecting AI-based risk analytics specifically to corporate debt financing in the Saudi Vision 2030 context while explicitly addressing explainability, data governance, model risk and human accountability.