Financial fraud poses significant threats to digital economies, necessitating intelligent real-time detection systems. Traditional models often fail to capture complex transaction patterns, motivating the development of graph-based neural architectures that analyze relational dependencies among entities for early and accurate fraud identification. An actual fraud detection model is developed that accurately identifies suspicious financial transactions by capturing dynamic user interactions, temporal patterns, and contextual behavior using Swarm Space Hopping-tuned Spectral Temporal Graph Neural Network Architecture (SSH-StemGNN-Arc), integrating temporal graph modeling with swarm-optimized hyperparameter tuning to capture complex fraud patterns in dynamic transaction graphs and improve classification accuracy under real-time constraints. Preprocessing involved removing duplicates, handling missing values using K-Nearest Neighbor (KNN) imputation, and encoding categorical variables using Smoothing Target Encoding (STE). All numerical fields were normalized to stabilize model training using min-max normalization. The SSH-SGNN-Arc model was implemented in Python, and attained 96.4% accuracy, 97.8% precision, 93.5% recall, and a 95.6% F1-score, outperforming baseline models. Real-time evaluation demonstrated its efficiency, with low latency and effective fraud flagging in dynamic transaction streams, confirming practical applicability. The SSH-SGNN-Arc framework effectively captures complex spatiotemporal dynamics in financial data. Through advanced graph-based learning and optimization, it enables real-time fraud detection with enhanced reliability, offering a scalable solution adaptable to diverse financial environments and evolving threat patterns.