Abstract: The increasing computational demands of artificial intelligence (AI) have intensified the search for sustainable, energy-efficient hardware solutions. Neuromorphic computing, inspired by the structure and function of biological neural systems, offers a promising alternative to traditional von Neumann architectures. This study conducts a comparative analysis of three leading neuromorphic paradigms: Hopfield Networks, Memristor-Based Architectures, and Spiking Neural Networks (SNNs), evaluating them across energy efficiency, scalability, training complexity, hardware maturity, and sustainability impact. Findings reveal that while Hopfield Networks provide robust memory solutions at small scales, Memristor-Based Systems and SNNs offer superior energy efficiency and potential for large-scale deployment. However, significant challenges remain, including the development of scalable training algorithms, the standardization of memristor fabrication, and the creation of supportive software ecosystems. The study emphasizes the necessity for hybrid architectures that integrate neuromorphic and traditional computing elements and calls for coordinated efforts in research, policy, and industry to address existing limitations. Ultimately, neuromorphic computing presents a transformative opportunity to align the future growth of AI with global sustainability goals, provided that multidisciplinary collaboration and innovation continue to advance the field.