<p>This study presents an advanced model based on the Particle Model approach to predict the compressive strength of fly ash concrete. This sophisticated model incorporates a classification system based on fuzzy logic-Gaussian similarity assessment, accurately evaluating chemical compositions, Particle Size Distribution (PSD), morphology, and compressive strength with 20% and 40% fly ash incorporation. Leveraging fuzzy hypergraph theory, this framework adeptly identifies the highest similarities among chemical compositions, PSD, and morphology of fly ash particles, offering a granular view beyond traditional Class C and F divisions. The predictive power of this model is rigorously assessed, emphasizing the differential impacts of various fly ash classes on concrete mechanical properties and their sensitivity to inherent fly ash characteristics. This detailed analysis aims for further optimization of fly ash in concrete mixes, based on regression classification for compressive strength prediction, leading to increased and more sustainable utilization of this industrial by-product in construction, in alignment with environmental sustainability goals through improved resource efficiency.</p>