U-Net Architecture is a convolutional neural network designed for biomedical image segmentation, featuring a symmetric encoder-decoder structure that allows for precise localization. It uses skip connections to combine high-resolution features from the encoder with the upsampled outputs, enabling effective learning from limited training data.