Urban Air Mobility leverages electric vertical takeoff and landing aircraft to relieve ground congestion and improve passenger and cargo transport within dense cities. Integrating UAM into existing airspace safely and efficiently requires ground control systems that deliver real-time situational awareness, risk assessment, and coordinated command across multiple vehicles. We introduce a distributed multi-agent architecture in which each vehicle functions as an autonomous agent, publishing its position, velocity, attitude, and flight plan over a scalable messaging layer. A recurrent neural network enhanced with attention and sinusoidal positional encoding consumes streaming data in an Earth-Centered, Earth-Fixed frame to produce multi-step trajectory predictions. Compared to the attention-only variant, positional encoding reduces mean squared error from 0.0525 to 0.0517, mean absolute error from 0.1680 to 0.1659, and root mean squared error from 0.2291 to 0.2273. Testing on 23 unseen UAM flights yields an overall RMSE of 0.1897 and an average inference time of 0.86 s, meeting real-time requirements. The system uses these predictions to compute three-dimensional separations and classify collision risk into Safe, Caution, Warning, or Collision categories, achieving 0.9881 classification accuracy. Throughput reaches 171.06 batches per second with end-to-end latency of 7.0 ms (95th percentile 6.5 ms), supporting both streaming and batch modes. To ensure interoperability and resilience, we employ the Model Context Protocol for standardized metadata exchange, synchronized state management, and distributed inference coordination. This protocol enables dynamic agent discovery, context-aware routing, and fault tolerance under network disruptions. By combining high-fidelity deep learning predictions with a protocol-driven multi-agent framework, our platform advances safe, scalable, and compliant UAM ground control in complex urban airspace.