This paper introduces a novel AI-driven approach for real-time early warning of trading behavior anomalies in financial markets. The proposed system integrates advanced deep learning architectures with traditional statistical methods to enhance detection accuracy and processing efficiency. Our framework employs a multi-layered neural network design optimized for high-frequency trading pattern recognition, incorporating feature extraction mechanisms specifically calibrated for financial market data streams. The system demonstrates exceptional performance, achieving a 97.5% detection rate for known trading anomalies while maintaining false positive rates below 1%. Performance evaluation confirms the system's ability to process approximately 150,000 transactions per second with average latencies of 15 milliseconds. Comprehensive testing against 24 months of historical market data validates the system's effectiveness across diverse market conditions, including high volatility and low liquidity scenarios. Comparative analysis reveals significant performance improvements over conventional surveillance methods, with detection accuracy increasing by 28% and processing efficiency improving by 45%. The system's adaptive learning capabilities ensure continuous evolution based on emerging trading patterns. Experimental results confirm robust performance across different market sectors, including stress-tested environments and cross-asset scenarios. This research advances market surveillance technology by establishing a new benchmark for real-time anomaly detection in complex financial ecosystems.