Laminated composite plates are extensively used in engineering applications because of their high strength-to-weight ratio and superior structural performance. However, stress concentration around cutout openings may reduce their durability and service life. Strengthening the cutout boundary through laminate lining offers an effective approach to improving structural integrity and vibration performance. This study investigates the fundamental natural frequencies of laminated composite plates with cutouts by comparing unlined and lined configurations, where the lining is achieved by increasing the number of laminate layers around the cutout edges. The lining factors are optimized using the Aqua Search meta-heuristic algorithm, while a topology optimization approach is employed to establish the convergence characteristics. The finite element formulation is based on a thin shell model without shear deformation using six-node linear strain triangular elements. Parametric analyses are performed under different loading conditions, including transverse and hygroscopic loading, and various boundary conditions, with particular emphasis on clamped laminated composite plates (CCCC). To validate the numerical results and evaluate predictive capability, five machine learning algorithms—Linear Regression (LR), Ridge Regression (RR), k-Nearest Neighbors (kNN), Random Forest (RF), and Gradient Boosting (GB)—are implemented in Google Colab®. Model performance is assessed using the coefficient of determination (R²) and root mean square error (RMSE). Among the investigated models, Gradient Boosting demonstrates the highest prediction accuracy, achieving an R² value of 99.9% and an RMSE approaching zero. The non-dimensional natural frequencies predicted by the machine learning models are in excellent agreement with the finite element results, confirming the reliability of the proposed computational framework. The study demonstrates that cutout edge lining significantly enhances the vibration characteristics of laminated composite plates and that machine learning provides an efficient and accurate tool for predicting their dynamic behavior.
