Rapid viral evolution and mutation pose continuous challenges to genomic surveillance and public health preparedness, while most existing computational approaches focus on analysing mutations only after they appear in real-world samples, limiting their ability to support early intervention. This paper presents the Quantum Bio-Mutation Foresight Simulator (Q-BMFS), a hybrid quantum-classical framework designed to explore and evaluate potential future mutation pathways before they naturally emerge. The proposed system combines quantum generative models with classical machine learning to simulate a large bio-mutation search space and assign probabilistic and risk-based interpretations to generated variants. Quantum Generative Adversarial Networks are employed to generate candidate mutation sequences, Quantum Circuit Born Machines are used to model mutation likelihood distributions, and Quantum Reservoir Computing captures temporal evolutionary behaviour, while a transformer-based classical model interprets long-range protein sequence dependencies. Experiments conducted using publicly available viral genome datasets and synthetically generated mutation samples demonstrate that Q-BMFS achieves improved predictive accuracy and more stable mutation likelihood modelling compared with classical baselines, and structural validation using RMSD analysis further indicates that the generated mutations preserve biologically plausible folding behaviour. These results suggest that quantum-enhanced foresight modelling can support proactive mutation analysis and may serve as a useful tool for future research in predictive virology and computational biology.