A Siamese Network is a specialized type of neural network architecture designed to process two similar inputs simultaneously. It consists of two identical branches that share the same parameters and weights, allowing the network to learn a similarity metric between the inputs. This architecture is particularly effective for tasks that require comparing or measuring the similarity of data, such as image recognition, signature verification, and voice recognition. By extracting relevant features from both inputs through shared layers, Siamese Networks can compute a similarity score, making them powerful tools in various machine learning applications.