The Vedic Multiplier, drawing inspiration from ancient Indian mathematical systems, has paved the way for innovative approaches in multiplier design. Researchers are increasingly focused on achieving high-speed performance, minimizing area, and reducing power consumption. This interest has led to the integration of diverse concepts and technologies into the Vedic Multiplier, making it a highly efficient design. This paper offers a comprehensive survey of recent Vedic multiplier architectures, examining their performance characteristics such as area and speed. It explores the fundamental principles behind several key Vedic mathematics sutras, including Urdhava Tiryakbhyam, Nikhilam Navatashcaramam, Ekadhikena Purvena and Anurupyena. Each of these sutras offers unique techniques for simplifying and speeding up the multiplication process. Urdhava Tiryakbhyam, for instance, uses a crosswise and vertical multiplication approach, while Nikhilam Navatashcaramam simplifies calculations by transforming numbers closer to base values. Ekadhikena Purvena provides an efficient method for squaring numbers and performing multiplications with specific patterns. Moreover, the paper extends its scope by investigating the application of Vedic mathematics in modern technologies, particularly in face recognition systems that utilize machine learning. With the use of Vedic mathematics, these systems can potentially enhance computational efficiency, reduce processing time, and improve accuracy. This intersection of ancient mathematical techniques with contemporary machine learning algorithms highlights the versatility and enduring relevance of Vedic mathematics in addressing complex computational challenges. Overall, this survey not only highlights the advancements in Vedic multiplier architectures but also showcases their practical applications in cutting-edge technologies, underscoring the significant impact of Vedic mathematics on modern computational methodologies.