Analytical methods for evaluating composite structures face complexities and limitations owing to the anisotropic nature of their material properties. Integrating numerical simulations with artificial intelligence (AI) techniques not only overcomes these limitations but also enhances design flexibility, diversity, speed, and accuracy. Tensile strength is a critical design parameter for such structures, and its precise determination significantly affects the design quality. This study calculates the tensile strength of glass/epoxy laminated composites using two approaches: Machine Learning (ML) and Response Surface Methodology (RSM). RSM is a widely used mathematical modeling technique to establish relationships between independent and dependent variables, while ML—a subset of AI—offers advanced predictive capabilities. Both methods require a Design of Experiments (DoE) and extensive datasets, which were optimized here using Latin Hypercube Sampling (LHS) to reduce sample tests while improving accuracy. Finite Element Analysis (FEA) was employed for dataset generation, validated through experimental tests. The developed models assessed the influence of design parameters (e.g., layer count and orientation) on tensile strength, demonstrating strong alignment with the dataset. This study accelerates the composite design process and enhances the understanding of mechanical behavior.