Random initialization is the process of assigning initial weights in neural networks to small random values, which helps break symmetry and ensures that different neurons learn different features. Proper random initialization can significantly impact the convergence speed and final performance of the model by avoiding poor local minima and ensuring a good starting point for optimization.