This paper explores the application of Grover’s Algorithm, a quantum search algorithm known for its quadratic speedup for string and keyword search tasks. While Grover’s Algorithm is theoretically efficient for unsorted data, its practical implementation in real-world text search reveals significant limitations. The study is divided into two phases: the first demonstrates the algorithm’s effectiveness in identifying specific letters from a small dataset using Google’s Cirq quantum simulator. The second phase extends the approach to word-level search within paragraphs, highlighting challenges in scalability, oracle design, and semantic interpretation. Although Grover’s Algorithm performs well for exact-match searches in small, structured datasets, it struggles with larger, semantically rich data due to the need for custom oracles and high qubit demands. Attempts to parallelize the algorithm do not yield true quantum speedup and instead mimic brute-force methods. The paper concludes by proposing Quantum Kernel Methods as a more scalable and semantically aware alternative. These methods leverage quantum feature maps to embed text in high-dimensional space, enabling classification based on meaning rather than exact matches. This shift addresses the core limitations of Grover’s Algorithm and opens pathways for quantum-enhanced AI content detection.