Quantum Machine Learning (QML) provides a promising computational paradigm to extract complex patterns from biomedical datasets by exploiting quantum superposition, entanglement, and parameterized variational circuits. Despite substantial theoretical work, no existing study provides a fully functional, deployable, and experimentally validated quantum–AI hybrid platform applied directly to real genomic cancer classification. Here, i introduce Q-Synapse, the first operational prototype that integrates: (1) an Angle-Encoded Variational Quantum Classifier (VQC) running on a Qiskit simulator, (2) a real Artificial Intelligence feature-selection engine (GradientBoosting-based genomic ranking), (3) PCA-driven quantum dimensionality mapping enabling training on 2–4 qubit manifolds, and (4) a classical baseline (SVM/MLP) for scientifically rigorous benchmarking. Using the Wisconsin Breast Cancer dataset and reduced TCGA-style gene-expression structures, Q-Synapse demonstrates that quantum circuits can achieve competitive or superior accuracy in low-dimensional genomic subspaces, with smoother convergence behavior and reduced sensitivity to noise. The integrated Streamlit interface provides real-time training visualization, confusion matrices, and feature-importance analytics, resulting in a complete, reproducible, and extensible quantum-biomedical research platform. Keywords Quantum Machine Learning; Variational Quantum Classifier; Genomics; Biomedical AI; Cancer Classification; Quantum AI Hybrid Systems; Streamlit; Qiskit.