The Convergence of Private Wireless with AI and Machine Learning: Investigating how private networks enable real-time data processing at the edge for AI-driven applications in manufacturing, predictive maintenance, and autonomous systems

The integration of Private 5G (P5G) networks with Edge Artificial Intelligence (Edge AI) signifies a fundamental change in industrial architecture, enabling the transfer of computational intelligence from centralized clouds to the network edge. This research paper examines how these technologies come together, focusing on their application in advanced manufacturing, predictive maintenance, and autonomous systems. By leveraging the deterministic connectivity provided by 3GPP Releases 16 and 17, specifically Time Sensitive Networking (TSN) and Ultra-Reliable Low-Latency Communication (URLLC), industrial companies can now support mission-critical AI workloads that require response times in the milliseconds range. We analyze architectural frameworks that leverage Multi-Access Edge Computing (MEC) for "split inference" and smart orchestration, as described by protocols such as OPC UA over 5G. Additionally, this study explores specific algorithmic enhancements in cloud robotics, including FogROS2 and OROS, which help reduce latency and energy consumption for mobile autonomous units. Case studies from John Deere, Hitachi, and Sandvik demonstrate the practical feasibility of these systems. The findings show that private wireless infrastructure serves not just as a connectivity link but also as a crucial data plane for real-time edge inference, enabling autonomous, self-optimizing factories and enhancing data sovereignty.