This paper proposes a new method to quantify the behavior of structural systems under fuzzy uncertainties using the Rao-kNNC algorithm. Rao algorithms are metaphor-less metaheuristics for solving global optimization. Owing to their simplicity, Rao algorithms have gained applications in various engineering design optimizations. However, their capability in fuzzy structural analysis is still uncovered. In this work, the Rao-1 algorithm is integrated with the α-cut strategy to establish an effective procedure to accurately estimate the fuzzy response of a structure with fuzzy parameters. Furthermore, a machine learning-based classification method, the k-nearest neighbor comparison (kNNC), is proposed to save the computation cost. The effectiveness of the proposed Rao-kNNC-based fuzzy procedure is confirmed by the numerical analysis of bar-like structures involving a relatively large number of fuzzy parameters. The membership function of fuzzy displacements and fuzzy internal forces developed in the structure’s members can be accurately estimated by the proposed method. It is also revealed that kNNC can significantly reduce the computational burden involving fuzzy structural analysis.