The optimization of NP-hard problems remains a fundamental challenge in computational science, as these problems cannot be solved in polynomial time using classical algorithms. The exponential growth of solution spaces renders traditional methods inefficient for large problem instances. Quantum computing, with its ability to leverage quantum parallelism and superposition, promises to offer substantial speedups for specific problem classes. However, quantum algorithms alone are often limited by factors such as noise, qubit coherence, and limited qubit counts. This paper proposes a hybrid quantum-classical framework that integrates the strengths of both classical and quantum computing to solve NPhard optimization problems efficiently. The proposed framework utilizes classical preprocessing techniques (e.g., heuristics and dynamic programming) to reduce problem complexity before applying quantum subroutines like Quantum Annealing (QA) or Quantum Approximate Optimization Algorithm (QAOA) to explore large solution spaces. After the quantum processor generates candidate solutions, classical postprocessing algorithms (e.g., genetic algorithms) further refine the results to ensure they meet realworld constraints. We demonstrate the effectiveness of this hybrid approach by evaluating it on three classic NP-hard problems: The Traveling Salesman Problem (TSP), Knapsack Problem, and Job Scheduling. Our experiments show that the hybrid framework outperforms traditional classical methods in terms of computation time and solution quality, especially for larger problem instances. This approach offers a promising path for solving complex optimization problems in practical applications, including logistics, manufacturing, and finance. The paper concludes with a discussion of the potential for scaling the framework to larger problem sizes and enhancing its robustness throughfurtherquantumhardwareadvancements