Extreme Ultra-Reliable Low-Latency Communication (XURLLC) is needed for the switch to sixth-generation (6G) wireless systems, which means that end-to-end (E2E) orchestration must be smooth. Most AI-assisted networking frameworks are reactive and centralized right now. This causes a lot of resource fragmentation and makes them a hundred times less reliable than 6G benchmarks. This article proposes the Cognitive Zero-Latency Orchestration Engine (CZOE) to fill this important performance gap. The CZOE is a fully autonomous architecture that combines Graph Neural Networks (GNNs) and Transformer models with a high-fidelity Digital Twin (DT). The framework uses Deep Reinforcement Learning (DRL) and a unique system for allocating resources that is robust predictive orchestration framework. In addition, a Fast Sender-Receiver Transmission System (F-SRTS) is used, which makes sure that control actions are activated 98.99% of the time right away. Experimental evaluations within a dynamic 6G simulation environment demonstrate that CZOE achieves a sustained reliability of 1-10^(-7), maintaining a 99.3% Service Level Agreement (SLA) compliance for mission-critical services with near-deterministic reliability. This performance represents a 78% improvement in Probabilistic reliability guarantee over traditional 5G Open Radio Access Network (O-RAN) orchestration, while reducing jitter and core signaling overhead by 95.6% and 95%, respectively. These results validate CZOE as a robust, AI-native solution for next-generation network orchestration.
