This research proposes a hybrid strategy that combines the K-means algorithm with Ant Colony Optimization (ACO) to improve clustering quality in complex datasets. Four approaches were evaluated: conventional K-means, K-means with mode initialization, K-means/ACO, and a hybrid method (K-means/ACO/mode), using Silhouette and Entropy metrics on three classic datasets. The results show that the hybrid approach achieves the greatest cohesion and separation between clusters, significantly reducing overlap and providing a controlled mechanism for balancing computational costs. This methodology offers a balance between accuracy and efficiency. Analysis of different datasets establishes a precedent for future applications that drive technological development, such as urban data segmentation, energy resource optimization, and real-time infrastructure management.