Cellular Automata (CAs) have emerged as one of the most straightforward computational models, distinguished by their proven capabilities to effectively simulate various complex physical systems and processes. CAs have two important properties-intrinsic parallelism and locality-that make them perfect for real-world computer architectures. They also make it easier for data to move between dedicated memory and processing cores, which is known as the von Neumann bottleneck. To leverage these advantages, extensive research has been conducted over the past decades, focusing on appropriate computer architectures, hardware implementations, and VLSI/FPGA applications. This chapter delves into the CAs and their abilities to deal with the aforementioned bottleneck, offering enhanced performance, especially when coupled with appropriate nanoelectronic, VLSI and FPGA technologies, circuits, and architectures. At the same time, the fact that CAs can be used for many different types of physical modeling tasks has led to the creation of many separate hardware implementations that aim to improve the performance of CA models in fields like physics, chemistry, ecology, geology, biology, and computer science. Furthermore, researchers have recently looked into new ways of doing things, like using memristors, oscillating circuits, quantum cellular automata (CA), and Graphene-based CA to build advanced hybrid CAs. These innovative methods hold the potential to further advance the pronounced capabilities of CAs, offering new dimensions of efficiency and functionality. Consequently, this chapter provides a comprehensive overview of CAs hardware also with a special emphasis on these recent advancements and their potential to revolutionize CA applications.