Unmanned Aircraft Systems (UAS) research increasingly requires high-performance, multi-agent simulation environments that integrate realistic dynamics with immersive visualization. This paper introduces a distributed co-simulation framework that seamlessly combines the dynamic modeling capabilities of Hopsan with the advanced rendering and interaction tools of Unreal Engine 5.3. Communication between the two platforms is achieved through a lightweight, User Datagram Protocol (UDP) based plugin, which supports bi-directional real-time data exchange and is complemented by a USB Raw Input plugin to integrate human-in-the-loop joystick control. The proposed framework was validated across progressively complex scenarios. First, a single F‑16 aircraft data model was imported from Hopsan, encompassing waypoint-guided dynamics, atmospheric effects, actuation, and geoid-based altitude calibration. Its state reinforced by Hopsan was visualized in Unreal in real-time. Second, the platform was extended to support two independent F‑16 agents, each communicating via dedicated UDP ports, thereby demonstrating modular, scalable, multi-agent operation. Third, we introduced a Human Machine Interface (HMI) scenario, where one aircraft was piloted manually via joystick input, while the second autonomously followed the same waypoint sequence. This validated the framework’s capacity to handle human interaction in a multi-agent context. Results evidence synchronized simulation at real-time performance, accurate environmental interactions (terrain, wind, collision), and reliable human-in-the-loop control. The framework’s architecture promotes modularity, scalability, and deployment flexibility across multiple machines. Future enhancements will explore tighter coupling with Unreal’s environmental physics, adoption of fluid dynamics, and scaling to larger agent ensembles. By integrating open-source dynamical modeling with high-fidelity graphical simulation, this platform offers a robust foundation for UAS mission planning, operator training, and AI-driven control validation.