Clustering is a fundamental concept of unsupervised learning, that helps in arranging similar objects into groups based on some similarity. Nevertheless, it is difficult to increase clustering efficiency for a large dataset. Therefore, the research combines QRDSO (Quantum-Driven Differential Search Optimization) and WAC-HACK (Weighted Adaptive Clustering using Hierarchical and K-means), presenting a hybrid framework of optimization. QRDSO employs quantum-based computation to enhance the exploring properties and convergence rates of hashing search in complex datasets, while WAC-HACK adjusts this clustering by using adaptive hierarchical approaches which guarantees an improved cluster assignment. These strategies jointly enhance the accuracy of clustering, reduce computational overhead, and aid the acquisition of data structure more effectively, especially in high-dimensional domains such as image analysis similar to TF-IDF which serves for text mining with bioinformatics. The proposed algorithm has improved its performance over existing techniques, making it a good candidate for large datasets and multi-dimensional clustering problems.