The growing demand for computational power has surpassed the capacity of traditional systems, creating an urgent need for innovative approaches. This study introduces a next-generation computing framework that integrates quantum-inspired algorithms, adaptive machine learning, and distributed architectures to achieve scalability, fault tolerance, and energy efficiency. The proposed framework reduced computation time by 40% (from 1050 ms to 630 ms for 10,000 tasks), improved fault recovery success to 95% (an 18.75% increase over baseline systems), and lowered energy consumption by 29.17%, aligning with global sustainability goals. Scalability tests confirmed the system’s capability to manage up to 1000 nodes—doubling the performance of standard architectures. These outcomes establish the framework’s potential for enhancing high-performance computing applications in cloud computing, AI-driven systems, and cybersecurity. Future work includes hybrid quantum-classical integration and domain-specific optimization to further improve adaptability and computational resilience.