This paper describes a hybrid architecture that combines Quantum Compressed Sensing (QCS) and Quantum Machine Learning (QML) to improve channel prediction in Massive MIMO-OFDM systems. The proposed method addresses the limitations of standard compressed sensing techniques, which rely heavily on strict sparsity assumptions, as well as machine learning models, which require large training datasets. Initially, QCS is used to achieve sparse channel recovery with fewer pilot observations, lowering overhead and improving spectral efficiency. The channel estimate is then revised using a QML-based model that is capable of learning nonlinear channel properties and adapting to dynamic propagation settings. This two-stage architecture allows for increased estimation accuracy in both sparse and non-sparse channel circumstances. The paradigm is notably relevant for the forthcoming 6G communication systems, which operate in high frequency bands with complex fading and noise behaviors. A hybrid loss function is used to optimize sparsity restrictions while also performing learning-based reconstruction. Simulation findings show that the suggested method reduces bit error rates and improves normalized mean square error when compared to standalone QCS and QML methods. Furthermore, the model exhibits robustness to noise and channel fluctuation, making it ideal for practical deployment. The combination of quantum inspired approaches with data-driven learning provides a scalable solution for next-generation wireless networks. In summary, the suggested method finds a balance between computing efficiency and estimation performance, while also opening up new possibilities for integrating model-based and learning-based paradigms into communication system design.