Weight initialization plays a critical role in training deep neural networks. Poor initialization can lead to vanishing or exploding gradients, symmetry issues, and slow convergence. In this article, we explore why common methods like zero, constant, and naive random initialization fail, and how principled approaches like Xavier (Glorot) and He initialization maintain stable signal flow and enable effective deep learning.