The integration of Artificial Intelligence (AI) into corporate finance is rapidly transforming how organizations forecast performance, manage governance structures, and optimize strategic decision-making. This paper explores the role of AI-driven models in enhancing predictive forecasting accuracy, strengthening corporate governance, and improving overall financial performance optimization. By leveraging machine learning algorithms, deep learning architectures, and advanced data analytics, corporate finance functions can move beyond traditional deterministic and econometric models toward adaptive, data-driven systems capable of learning from complex, high-dimensional financial data. AI-enabled predictive forecasting supports more accurate revenue projections, cash flow estimation, risk assessment, and scenario planning, even under volatile market conditions. From a governance perspective, AI tools enhance transparency, internal controls, and compliance monitoring by detecting anomalies, fraud patterns, and governance inefficiencies in real time. Furthermore, AI-based optimization models facilitate capital allocation, cost efficiency, and portfolio management by continuously evaluating performance trade-offs and strategic constraints. This study synthesizes emerging theoretical frameworks and applied use cases, highlighting how AI integration reshapes corporate finance into a proactive, intelligence-driven discipline. The paper also discusses implementation challenges, including data quality, model explainability, ethical considerations, and regulatory alignment, offering insights into best practices for sustainable adoption. Overall, AI-driven corporate finance models represent a critical pathway for organizations seeking resilient governance, predictive intelligence, and long-term value creation.