This study explores the prediction of stress values in glass fiber and epoxy resin laminated composites subjected to tensile loads through advanced artificial intelligence (AI) models. Accurate stress prediction is essential for the design and analysis of composite materials, emphasizing the need for efficient and precise computational methods. To this end, finite element analysis (FEA) software was utilized to simulate the effects of various lamination sequences on composite samples, resulting in a detailed dataset. This dataset was subsequently used to train and evaluate three AI models: Narrow Neural Network (NN), Squared Exponential Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The stress predictions from these AI models were compared against those derived from FEA simulations and validated with experimental data. The results revealed that the GPR and SVM models outperformed the NN model, delivering superior predictive accuracy. Specifically, the GPR and SVM models achieved prediction accuracies of 96.83% and 95.04%, respectively, underscoring their potential for enhancing the analysis of composite materials.