Research on forecasting corporate financial performance has rushed from traditional econometric models toward machine learning, deep learning, and high-precision hybrid AI architectures. These methods can capture nonlinear relationships, high-dimensional structures, and regime shifts in financial data more effectively, which has driven their widespread adoption. At the same time, practical requirements for interpretability, regulatory transparency, and model risk governance have made explainable AI an essential component of modern forecasting systems. This Structured Literature Review synthesizes ninety-three empirical studies published between 2000 and 2025 using a PRISMA-informed selection procedure. It evaluates the actual contributions of hybrid AI and explainable AI to corporate financial performance forecasting. The review shows that econometric and machine learning hybrids, ensemble learning models, DEA-based machine learning frameworks, deep learning combined with signal processing, and multimodal architectures are extensively used and collectively improve predictive accuracy and stability. Methods such as SHAP, LIME, partial dependence, and individual conditional effect analyses, attention mechanisms, and counterfactual reasoning significantly enhance model interpretability, support managerial decision-making, and strengthen compliance with regulatory expectations. Despite these advances, challenges remain, including the predominance of static data analysis, limited generalizability, and the lack of architectures designed for realistic deployment. Future research should focus on multimodal data integration, causal AI, adaptive, real-time learning frameworks, and explainable hybrid systems aligned with regulatory and governance requirements.