Although Random Forest (RF) is a widely adopted ensemble method for classification, preserving diversity among decision trees and reducing the negative impact of underperforming trees remain major challenges. This paper proposes DWRF-MVC, a novel RF optimization framework that integrates DBSCAN clustering with a multi-metric performance-weighted voting mechanism. Using a dissimilarity metric derived from classic RF outcomes, the framework applies DBSCAN to cluster decision trees, selects top-performing representatives from each cluster, and retains noise points to maintain diversity. A weighted voting mechanism is then introduced to further improve model performance. Experimental results on 14 benchmark datasets show that DWRF-MVC achieves an average accuracy improvement of 7.45% over classical RF with a 91.79% reduction in the number of trees. Moreover, DWRF-MVC surpasses two cutting-edge methods by 1.82% and 3.83% in average accuracy, respectively.