This paper presents the use of artificial neural networks (ANNs) to analyze the buckling behavior of composite plate elements with cut-outs, which can function as spring elements. The analysis is based on numerical results. ANNs models were developed using numerical data to predict the flexural-torsional buckling form of composite plates under different cut-out and fiber angle configurations. The models were trained and tested using a large dataset, and their accuracy was evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angle configurations under compression. The combination of numerical analysis with ANNs models provides a practical and efficient solution for evaluating the stability behavior of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications. The study focuses on a rectangular carbon-epoxy composite plate with a central cut-out, featuring adjustable geometric dimensions. The plate's composite material has anisotropic properties, and the laminate structure is arranged in a non-symmetric sequence. The critical force and buckling form were determined using FEM in the Abaqus program. The results were compared with those from a previous paper using an analytical method. The ANNs models were used to predict the critical force and buckling form of the composite plate. The first model, Model I, was designed to predict the critical force values, while the second model, Model II, was a classification model to identify the first buckling form. The results showed high accuracy in both models, with Model II achieving 100% classification accuracy. The ANNs models demonstrated excellent performance in predicting the critical force and buckling form of the composite plate. The integration of numerical analysis with ANNs models offers a viable and effective approach for assessing the stability characteristics of composite plates with cut-outs, which can be applied to various engineering applications. The results indicate that ANNs models are a robust tool for modeling critical force and predicting buckling forms in composite plates.This paper presents the use of artificial neural networks (ANNs) to analyze the buckling behavior of composite plate elements with cut-outs, which can function as spring elements. The analysis is based on numerical results. ANNs models were developed using numerical data to predict the flexural-torsional buckling form of composite plates under different cut-out and fiber angle configurations. The models were trained and tested using a large dataset, and their accuracy was evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angle configurations under compression. The combination of numerical analysis with ANNs models provides a practical and efficient solution for evaluating the stability behavior of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications. The study focuses on a rectangular carbon-epoxy composite plate with a central cut-out, featuring adjustable geometric dimensions. The plate's composite material has anisotropic properties, and the laminate structure is arranged in a non-symmetric sequence. The critical force and buckling form were determined using FEM in the Abaqus program. The results were compared with those from a previous paper using an analytical method. The ANNs models were used to predict the critical force and buckling form of the composite plate. The first model, Model I, was designed to predict the critical force values, while the second model, Model II, was a classification model to identify the first buckling form. The results showed high accuracy in both models, with Model II achieving 100% classification accuracy. The ANNs models demonstrated excellent performance in predicting the critical force and buckling form of the composite plate. The integration of numerical analysis with ANNs models offers a viable and effective approach for assessing the stability characteristics of composite plates with cut-outs, which can be applied to various engineering applications. The results indicate that ANNs models are a robust tool for modeling critical force and predicting buckling forms in composite plates.