Forward propagation is the process of passing input data through a neural network to obtain an output prediction. It involves sequentially propagating the information from the input layer, through the hidden layers, and to the output layer. Each neuron in the network calculates a weighted sum of its inputs, applies an activation function, and passes the result to the next layer. This iterative process continues until the output layer produces the final prediction. Forward propagation allows the network to transform input data into meaningful output predictions by utilizing learned weights and activation functions at each layer.