Multiple layers in neural networks refer to the arrangement of interconnected neurons organized into distinct layers, each performing specific functions. The foundational layer, known as the input layer, receives raw data, while subsequent layers, called hidden layers, process this information through complex transformations. The final layer, the output layer, generates predictions or classifications based on the processed data. This multi-layered architecture enables neural networks, particularly Multilayer Perceptrons (MLPs), to learn intricate patterns and relationships within data, making them powerful tools for tasks such as image recognition, natural language processing, and more.