Fingerprint recognition is a cornerstone of biometric authentication systems due to its distinctiveness and reliability. However, the computational complexity of current fingerprint matching algorithms presents significant challenges, particularly in large-scale databases, where a vast number of comparisons is required. This paper proposes an innovative approach to improve the efficiency of fingerprint matching by leveraging pairwise minutiae feature indexing within a three-dimensional (3D) cubic framework. The proposed method encodes fingerprint features as a set of indirect features derived from minutiae pairs, which are invariant to rotation and translation. These features are then indexed within a cubic structure, enabling efficient identification of matching candidates through a targeted mini-cube search. This approach substantially reduces computational complexity while enhancing matching accuracy. Experimental results demonstrate that the proposed method achieves a penetration rate of 3.29% at a 100% hit rate across multiple databases, outperforming existing techniques. The findings indicate that this framework significantly enhances fingerprint matching performance, making it a promising solution for large-scale biometric systems in diverse real-world applications.