The exponential growth of AI and machine learning has intensified demands on computational resources, particularly multiply-accumulate (MAC) operations in deep neural networks. This paper investigates integration of Vedic Mathematics—a 16-sutra ancient Indian system—into modern AI algorithms. Through systematic analysis of empirical studies, we demonstrate that Vedic techniques offer substantial improvements: CNNs using Vedic multiplication achieve 9.5% higher accuracy and 6.5% lower delay; Vedic multiplier-based DNNs reduce propagation delay by 23.5%; Vedic processors cut power consumption by 35% and thermal resistance by 40%; and Vedic-inspired state space models outperform 28 contemporary benchmarks. Vedic Mathematics provides mathematically rigorous, computationally efficient alternatives, particularly valuable for resource-constrained AI inference.