Deep learning models are the most important part of current AI systems because they can learn complex patterns from data in a very impressive way. However, creating these models often takes a lot of time and computer power, especially when methods like Batch Normalization (BN) are used. By balancing activations, BN helps to stabilize and speed up neural network training, but it adds extra work to the computer, which slows down convergence speed. This paper looks at different ways to speed up the convergence of deep learning models that use BN. This study looks into different ways to slow down the convergence process caused by BN layers by reading a lot of current research and doing experiments. Some methods, like weight normalization, group normalization, and layer normalization, are looked at to see if they can be used instead of or in addition to BN to keep or speed up convergence. It also looked into how to change the architecture and make optimization techniques that work with these different standardization methods. Our results show that while BN is still a popular way to keep neural network training stable, other methods show promise for speeding up convergence without affecting performance. By using these other approaches, professionals can cut down on the amount of work that needs to be done on computers and the time it takes to train models. This makes it easier to build and use deep learning models in places with limited resources. This study adds to the ongoing search for deep learning methods that work well and can be used by many people. It also opens up new areas for research and improvement in model training.