Padding and strides are key concepts in convolutional neural networks that control spatial dimensions and efficiency. This article explains why padding preserves boundary information and spatial size, how zero padding works mathematically, and how stride reduces feature map resolution. With clear intuition and formulas, it shows how padding maintains detail while strided convolution enables efficient downsampling.