Neural Networks (NNs) are the most promising systems in this era of Artificial Intelligence (AI) and automation. Neural Networks are the replication of the human brain and nervous system as these imitate the processing and architecture of the human brain and natural intelligence. Machine Learning (ML) and Deep Learning (DL) are buzzwords for the current market trends and Neural Networks are the soul of these buzzwords. This article is dedicated to proposing a novel implementation of artificial neural networks using Vedic Mathematics for fast processing and better accuracy. The ML and DL models work well with enough large datasets. And, to process large datasets, these models take high computational time. Hence, the authors propose a Vedic Mathematics-Based Neural Network Design (i.e., VedNNet) improving the performance of ML/DL models and consuming less computational time. The proposed design is solely based on Vedic sutras and operations. The simulation results show that the proposed model named VedNNet is faster in comparison to the traditional neural network by 23.5% in delay time noted in nanoseconds. .