<p>Clustering, a fundamental unsupervised learning technique, aims to identify meaningful patterns within datasets. However, selecting the most suitable clustering approach remains challenging because it depends on specific optimization criteria. Clustering ensemble methods address this by combining multiple clustering models to enhance stability and accuracy, yet including low-quality models can degrade overall performance. To overcome this limitation, we propose a novel hybrid clustering framework based on consensus-driven genetic clustering. The framework comprises three key phases: generating diverse <i>k</i>-genetic clustering models with randomized parameters to enhance diversity; merging models using a consensus function that leverages similarity measures; and evaluating the final clustering quality using multiple assessment metrics. Our framework improves clustering accuracy by incorporating genetic algorithms and ensemble-based consensus techniques while maintaining robustness. Experimental evaluations on sixteen benchmark datasets demonstrate its effectiveness, showing an average improvement of 11% in normalized mutual information (NMI), 10% in adjusted rand index (ARI), and 7% in F1-score compared to traditional clustering methods. These results confirm that our proposed approach provides a more reliable and high-quality clustering solution by leveraging the synergy between genetic and consensus-based learning techniques.</p>