Structural damage identification and optimization in truss systems have increasingly relied on metaheuristic algorithms because of the high nonlinearity, multimodality, and computational complexity associated with conventional optimization methods. Among these approaches, swarm intelligence–based algorithms inspired by natural foraging and survival behaviors have demonstrated considerable effectiveness in solving continuous optimization problems. In this study, the African vultures optimization algorithm, a bio-inspired metaheuristic algorithm modeled on the navigation, foraging, and cooperative hunting behaviors of African vultures, was applied to the structural analysis and damage identification of planar and space truss systems. Finite element analysis was integrated with the optimization framework to evaluate the structural dynamic responses and natural frequency variations associated with different damage scenarios. Structural damage was identified through the minimization of objective functions. The exploration and exploitation capabilities of the African vultures optimization algorithm were systematically utilized to enhance global search performance and convergence stability during the optimization process. The effectiveness and robustness of the proposed approach were assessed through several benchmark truss structures subjected to varying damage conditions. Accurate localization and quantification of structural damage were achieved with high computational efficiency and strong convergence behavior. In comparison with conventional metaheuristic optimization techniques, improved stability, reliability, and solution accuracy were observed, particularly in complex and high-dimensional structural optimization problems. The findings demonstrate that the African vultures optimization algorithm can serve as an efficient and reliable computational tool for structural health monitoring, vibration-based damage detection, and optimization of truss structures. The proposed framework is expected to provide significant potential for advanced engineering applications involving large-scale structural systems and intelligent damage assessment.