Accurate modeling of guided wave propagation is crucial for structural health monitoring (SHM) systems, where a large amount of information and cases are needed to cover all in-service conditions of a structure. Finite-element models have proven to be accurate enough to simulate the problem; however, they typically require substantial computational resources, and each simulation may require a significant amount of time. This article presents a comprehensive study of a ray-tracing-based wave propagation methodology applied to predict the acoustic behavior of lightweight structures. Focused on composite materials, particularly carbon fiber-reinforced plastic (CFRP), the study addresses the growing need for accurate and fast simulation tools in industries where high-strength lightweight materials play a pivotal role, such as aerospace or automotive. The study presents an examination of the ray tracing method’s effectiveness with series of experimental coupon tests, ranging from a simple metallic plate to a representative CFRP wing lower cover of the Universidad Politécnica de Madrid-LIBIS Unmanned Aerial Vehicle. The investigation spans a distribution of possible damage locations ensuring a comprehensive applicability evaluation. Results demonstrate efficacy in predicting the wave propagation characteristics, including transmission, reflection, and absorption within composite structures, and also an accurate representation of its behavior for in-service damages, both via added masses and real impact damages. The validation involves an in-detail comparison with experimental measurements, evaluating the reliability and applicability of the ray tracing approach. This research not only contributes to the advancement of predictive modeling for acoustic behavior in composite structures but also addresses the broader implications for industries relying on accurate simulations for design optimization and performance evaluation. The validated ray tracing method has proven to be a valuable tool to ensure precise predictions of wave propagation in composite materials, and its computation speed makes the methodology ideal to contribute to a training database for a possible physics-informed machine learning SHM system.