Transformer Networks are a type of neural network architecture that relies on self-attention mechanisms to process input data, enabling parallelization and improved performance on tasks like natural language processing. They have revolutionized the field by allowing models to capture long-range dependencies and contextual information more effectively than previous architectures like RNNs and LSTMs.