Quantum neural networks (QNNs) are being added to 6G and 7G communication systems as a way to meet the growing demand for very low latency, fast data rates, and low energy use in next-generation networks. This chapter introduces a new framework that combines QNNs with tree-based ensemble learning algorithms, such as gradient boosting and random forests, to improve network performance and resource allocation in communication environments that are always changing and becoming more complicated. While tree-based ensemble learning offers strong, interpretable, and adaptive models for managing large datasets and reducing classical computational bottlenecks, the proposed framework uses the quantum parallelism and superposition properties of QNNs to improve decision-making procedures. Tree-based algorithms ensure scalability in this work’s hybrid quantum-classical architecture, while QNNs handle feature extraction and optimization in high-dimensional spaces. While lowering computational overhead, this mix allows real-time adaptation to network conditions, including changing channel states and user mobility. The simulation results show big improvements in key performance indicators like spectral efficiency, energy consumption, and latency compared to traditional machine learning and simple quantum approaches. In addition, this work looks into the theories behind quantum-classical synergy, which sheds light on how the structure converges and how strong it is. The results demonstrate that we can use QNNs and tree-based ensemble learning to solve problems in very dense, heterogeneous communication systems. They do this by showing how well they work and how easily they can be scaled up for the next 6G/7G networks. This work provides a fresh approach to leveraging quantum benefits in practical applications, supporting the developing discipline of quantum machine learning in telecommunications.