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This research presents a Quantum-Inspired AI model designed to solve grid-based puzzles like Sudoku more efficiently. By combining classical AI techniques with quantum-inspired optimization methods such as QAOA and VQE, the model enhances problem-solving speed and accuracy. It integrates deep learning, reinforcement learning, and symbolic reasoning to mimic human cognitive processes. The system is capable of handling structured logic and adapting across various puzzle formats. Results show improved performance over traditional AI Models. This approach has a pplications in education, cybersecurity, and AI-assisted decision-making. The model also incorporates Quantum Neural Networks (QNNs) for better pattern recognition and learning in ambiguous or incomplete puzzle states. A modular framework built with tools like Qiskit and TensorFlow Quantum allows for easy experimentation and hybrid integration. Performance evaluations highlight gains in both computational efficiency and interpretability. The system demonstrates robustness across different puzzle complexities and constraint types. Its scalability and adaptability make it suitable for real-world problem-solving in logistics, planning, and intelligent tutoring systems.