Automated Fiber Placement (AFP) revolutionizes composite manufacturing by offering precise, efficient, and scalable solutions for additively manufactured aerospace components. The ability to rapidly manufacture lightweight and high-performance structures cements AFP as a leading manufacturing process of the future. Despite its advantages, AFP is prone to defects that can affect mechanical properties, particularly when defect stacking occurs through the thickness. This stacking can compromise structural integrity, increase weight, and degrade surface quality, necessitating costly shimming operations. Developing computational methodologies to capture green-state thickness would enable prediction of post-cure thickness, ultimately increasing throughput by allowing the prediction of shimming, thus meeting specified tolerances. This study introduces a green-state thickness model for AFP-manufactured components, constructed using data from the Automated Composite Structure Inspection System (ACSIS). ACSIS employs laser profilometry to scan part surfaces, generating topographical data to determine thickness. By referencing known gap, overlap, and pristine regions, the raw profilometry data is interpolated into laminate thickness values across the surface. Additionally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to identify and remove instances of noise in the model. The proposed inspection-based model not only provides an input for engineers to calculate post-cure part thickness but also a stepping-stone toward more intensive computation for predicting defect interaction and morphology.