<p>Data mining provides insights from massive and complex data to help make informed decisions, find hidden patterns, and solve difficult real-world problems. Unsupervised clustering groups natural data without labeling. Classic clustering algorithms like K-Means are popular due to their simplicity and computational efficiency, but they are sensitive to initialization and cannot handle noisy or non-convex cluster topologies. A hybrid clustering technique integrating L-SHADE, Bacterial Memetic Optimization (BMO), and K-means initialized HDBSCAN overcomes these concerns. The proposed system uses L-SHADE’s adaptive parameter management and convergence efficiency, BMO’s exploration–exploitation balance and memetic learning, HDBSCAN’s density-aware, noise-resilient clustering, and K-Means for centroid initialization Improve global and local search performance, convergence, avoid premature stagnation, and clustering in noisy, high-dimensional data with hybrid. Eleven popular benchmark datasets were utilized to evaluate LS-BMO-HDBSCAN. The recommended technique was compared against K-Means, PSO, NM-PSO, K-PSO, K-NM-PSO, CPSO, BFO, IBFO, BCO, and SMBCO. Performance was measured using Silhouette Score, Davies-Bouldin Index, Rand Index, Jaccard Index, and objective function. The durability, adaptability, and clustering accuracy of LS-BMO-HDBSCAN are confirmed by experimental results that outperform other methods across all datasets. This approach solves complex clustering problems in real-world data mining intelligently and reliably.</p>