The vanishing gradient problem occurs when gradients of the loss function become too small during backpropagation, making it difficult for neural networks to learn and update weights in earlier layers. This issue is particularly prevalent in deep networks with activation functions like sigmoid or tanh, leading to slow convergence or complete stagnation of training.