Detecting and locating damage is essential in maintaining structural integrity. While Artificial Neural Networks (ANNs) are effective for this purpose, their performance can be significantly improved through advanced optimization techniques. This study introduces a novel approach using the Grasshopper Optimization Algorithm (GOA) to enhance ANN capabilities for predicting defective aluminum plates. The methodology begins by deriving input parameters from natural frequencies, with defect locations as the output. A Finite Element Model (FEM) is used to simulate data by varying defect locations, creating a comprehensive dataset. To validate this approach, experimental data from vibration analyses of plates with different defect locations is collected. We then compare the performance of our GOA-optimized ANN against other metaheuristic algorithms, such as the Cuckoo Search Algorithm (CSA), Bat Algorithm (BA), and Firefly Algorithm (FA). Notably, CSA’s performance is slightly close to GOA. The results show that our GOA-based method outperforms these traditional algorithms, demonstrating superior accuracy in damage prediction. This advancement holds significant potential for applications in structural integrity monitoring and maintenance.