The Attention Mechanism is a crucial component in neural networks that enables the model to focus on specific parts of the input sequence when making predictions. By assigning varying levels of attention to different elements, the model can prioritize important information and improve performance on tasks that require understanding context and relationships. This mechanism is widely used in sequence-to-sequence models, such as those for machine translation and text generation, where it helps capture relevant information from long sequences and enhances the quality of generated outputs.