Traditional language teaching methods often fall short in meeting modern educational demands. These approaches are further restricted by data processing delays, limited feedback mechanisms, and accessibility issues. To address these challenges, this study introduces Nex-G QSLO (Next Generation Quantum Synergetic Learning Optimizer), a novel intelligent English teaching optimization system that integrates the advantages of 6G-enabled wireless systems, quantum machine learning (QML), and immersive learning technologies. The proposed model utilizes Quantum K-means (QKM) for effective clustering and Quantum Support Vector Machine (QSVM) for precise prediction and personalized recommendation. By utilizing 6G Ultra Reliable Low Latency Communication (URLLC) and edge computing, the system ensures effective data transmission and real-time feedback. Interactive technologies like VR and AR further enhance student engagement and learning involvement. The model also highlights its ability in advanced data security through quantumresistant encryption. Simulation is conducted using the UCI College English Teaching dataset. When compared to baseline models, the proposed Nex-G QSLO achieves a 4.32% improvement in accuracy, a 6.57% increase in F1-score, and a 5.46% enhancement in AUC and demonstrates its superiority in optimizing English language instruction.