Why Resnet18 works better than VGG16 architecture? Based on https://towardsdatascience.com/vggnet-vs-resnet-924e9573ca5c and
my understanding, Resnet18 architecture is faster because it is based on skip connections which not allow vanishing gradient problem to occur.
My question is, VGG16 uses Relu activation functions at each layer than how vanishing gradient problem occurs in it?
Please correct me if I'm wrong. Suggestions are welcomed!!

They both use ReLu and without skip connections which allow gradients to backpropagate around blocks, gradient vanishing is inevitable with increasing network depth. ReLu doesn't really "solve" gradient vanishing, there's just a lesser chance of it happening compared to a sigmoid activation function for example.
