Multiple layers in neural networks refer to the arrangement of interconnected neurons organized into distinct layers, each serving a specific function in processing data. The foundational layer, known as the input layer, receives raw data, while one or more hidden layers perform complex transformations and feature extraction. Finally, the output layer generates predictions or classifications based on the processed information. This layered architecture allows neural networks, particularly Multilayer Perceptrons (MLPs), to learn intricate patterns and relationships within data, making them powerful tools for various applications in machine learning and artificial intelligence.