Radial Basis Function (RBF) is a type of artificial neural network designed to handle nonlinearly separable data. It consists of one input layer, one output layer, and a single hidden layer using nonlinear activation functions. RBF is utilized for tasks like interpolation, classification, and time series prediction, requiring weight adjustments through backpropagation for optimization.