Reconstructing accurate 3D vein structures is essential for advancing automated healthcare interventions like intravenous cannulation. Existing techniques struggle with noisy grayscale vein images and lack robust algorithms tailored to medical datasets. We present Vein Prediction and Keypoint Matching (VPKM), a hybrid algorithm combining image denoising using an Encoder–Decoder and efficient keypoint detection through AKORB (AKAZE for detection and ORB for descriptor generation). VPKM achieves an F1 Score of 0.365, outperforming traditional methods in precision and computational efficiency. Tested on a near-infrared imaging dataset with real-time performance under 50 milliseconds, VPKM significantly improves keypoint matching accuracy, enabling reliable 3D vein reconstruction. These advancements highlight VPKM’s potential to transform image-based medical procedures by enhancing precision and reducing manual dependency.