Early diagnosis of structural damage, particularly in identifying its location, is essential for timely repair and maintenance. A vibration-based approach is effective, as damage alters a structure’s dynamic properties. Among these, mode shape-based methods offer faster, simpler localization than frequency-based ones. This study proposes a statistically based approach to enhance damage localization by applying a threshold to suppress false peaks in undamaged areas. Numerical studies on two beam-like structures confirm its superior accuracy compared to the modal curvature and mode shape curvature square methods. The method's robustness is validated under varying conditions, such as different mode numbers, sensor sparsity, and damage levels. To quantify damage extent, an artificial neural network (ANN) model optimized using a stochastic algorithm is employed. The optimized ANN achieves less than 2% error, even with added white Gaussian noise. The findings confirm the efficiency and reliability of the proposed approach in both localizing and quantifying structural damage.