<p>Fibre-reinforced polymer (FRP) composites are susceptible to impact damage during their service life. Vibration-based structural health monitoring (SHM) has emerged as a promising technique for reliably detecting and assessing such damage. This approach leverages the principle that damage induces local stiffness discontinuities, altering vibrational parameters such as natural frequencies, mode shapes, and damping. Research in this area has focused on using frequency shifts across multiple modes and applying inverse algorithms like Artificial Neural Networks (ANN) or Genetic Algorithms (GA) to predict the location and extent of damage. Training these machine learning algorithms (MLAs) requires extensive databases of frequency shifts corresponding to various damage parameters. To address the computational demands of database generation, numerical models offer an economical alternative. This study introduces a novel numerical methodology for evaluating frequency shifts in FRP composite laminates subjected to impact damage, facilitating machine learning-based non-destructive evaluation. The approach employs finite element modelling with restart analysis to efficiently simulate multiple damage scenarios without the need for complex parametric mappings. Simulations of composite plates under various impact energies demonstrate the method's effectiveness in characterizing cumulative damage through frequency shifts, particularly at higher energy levels, where it outperforms conventional techniques. The study also examines the influence of different damage criteria, including two-dimensional and three-dimensional Hashin models, on frequency shifts and mode shapes, showcasing the versatility of the proposed method. By streamlining the generation of extensive frequency shift databases, this approach significantly enhances the training of MLAs for inverse damage identification. The methodology has the potential to advance SHM techniques for composite structures across aerospace, automotive, and wind energy applications.</p>