<p>The financial markets’ quick growth and the rising volatility of corporate environments have intensified the challenge of achieving accurate financial risk forecasting. Conventional models often struggle to handle non-linear dependencies, high-dimensional relationships, and reduced predictive reliability in uncertain market conditions. In order to address these issues, this paper presents a unique framework called “Enhancing Corporate Financial Risk Forecasting under Reduced Predictive Reliability Using Residual Knowledge Single-Head and Dandelion Vision Graph Neural Network (RKSH-DVGNN)”. A custom dataset, named “Enhanced Corporate Financial Risk Forecasting Dataset under Reduced Predictive Reliability”, has been specifically constructed for this research. The dataset is generated based on synthetic transactions that simulate the financial activities of companies in terms of cash flow, credit exposure, debt repayment, liquidity changes, and transaction shocks triggered by market changes. The generation process of synthetic transactions used controlled stochastic modeling techniques to generate realistic time-series and network properties of financial transactions while maintaining privacy and availability of the data. Compared with other traditional publicly available financial datasets, this dataset provides an advantage in its ability to incorporate structured knowledge of market volatility, dependencies between companies, and regulatory stresses to enhance resilience in training and benchmarking models under predictive uncertainties. The main reason for generating this dataset is to develop a replicable and privacy-protected environment that can capture various risk indicators in stable and turbulent markets that cannot be captured by traditional datasets because of their data sensitivity and incompleteness issues. Reversible Automatic Selection Normalization (RASN) is recommended for data preparation in order to reduce the impact of systemic and stochastic disturbances brought on by irregular transaction frequency, outlier price spikes, and value that is missing distortions. Next, PTMTTaxoFormer is applied to conduct advanced hierarchical feature extraction, thereby allowing multi-level learning of taxonomic dependencies between financial metrics, organizational hierarchy, and risk categories. By the term “advanced,” one means its prompt tuning ability, which involves adaptive fine-tuning of transformer attention heads according to specific task-related cues and thus enables a dynamic balance between local and global financial interactions. RKSH-DVGNN represents a knowledge graph-based GNN along with a residual attention single-head vision transformer network (RA-SHViT-Net). The model exploits relations between financial entities and their visual and temporal dependencies in risk dynamics. Meanwhile, the Dandelion optimizer algorithm (DOA) allows fast and robust convergence by balancing exploration and exploitation processes during optimization. According to the experimental analysis, the proposed RKSH-DVGNN framework achieves an accuracy of 98.70% in predicting corporate financial risks and demonstrates competitive performance compared with existing approaches. The research shows that using KG-GNN, RA-SHViT-Net, and DOA together improves risk detection and prediction. This combo handles different financial situations really well, making the system very reliable. So, this integration enhances both accuracy and adaptability in different scenarios. The main strengths of the RKSH-DVGNN model include its stability in uncertain conditions, robustness against noise in pre-processing, and efficient performance in high-dimensional financial data space.</p>