Laser　speckle　contrast　imaging　(LSCI)　is　an　optical　technique　used　to　assess　blood　flow　perfusion　by　modeling　changes　in　speckle　intensity,　but　it　is　generally　limited　to　qualitative　analysis　due　to　difficulties　in　absolute　quantification.　Three-dimensional　convolutional　neural　networks　(3D　CNNs)　enhance　the　quantitative　performance　of　LSCI　by　excelling　at　extracting　spatiotemporal　features　from　speckle　data.　However,　excessive　downsampling　techniques　can　lead　to　significant　information　loss.　To　address　this,　we　propose　a　hybrid　quantum-classical　3D　CNN　framework　that　leverages　variational　quantum　algorithms　(VQAs)　to　enhance　the　performance　of　classical　models.　The　proposed　framework　employs　variational　quantum　circuits　(VQCs)　to　replace　the　3D　global　pooling　layer,　enabling　the　model　to　utilize　the　complete　3D　information　extracted　by　the　convolutional　layers　for　feature　integration,　thereby　enhancing　velocity　prediction　performance.　We　perform　cross-validation　on　experimental　LSCI　speckle　data　and　demonstrate　the　superiority　of　the　hybrid　models　over　their　classical　counterparts　in　terms　of　prediction　accuracy　and　learning　stability.　Furthermore,　we　evaluate　the　models　on　an　unseen　test　set　and　observe　that　the　hybrid　models　outperform　the　classical　models　with　up　to　14.8%　improvement　in　mean　squared　error　(MSE)　and　up　to　26.1%　improvement　in　mean　absolute　percentage　error　(MAPE)　evaluation　metrics.　Finally,　our　qualitative　analysis　shows　that　the　hybrid　models　offer　substantial　improvements　over　classical　models　in　predicting　blood　flow　at　both　low　and　high　velocities.　These　results　indicate　that　the　hybrid　models　possess　more　powerful　learning　and　generalization　capabilities.