This study presents a comprehensive multi-criteria decision analysis (MCDA) of fifteen composite particleboard materials based on their fundamental physical and mechanical properties. The evaluation utilized Grey Relational Analysis (GRA) to systematically rank particleboard compositions according to five critical performance parameters: density (D), water absorption (WA), thickness swelling (TS), modulus of rupture (MOR), and modulus of elasticity (MOE). The Grey Relational Grade (GRG) methodology revealed significant performance variations among different particleboard compositions. The analysis identified sawdust waste reinforced with plastic-based resin (waste styrofoam) as the optimal composition, achieving the highest GRG value of 0.8143 (81.43%), indicating superior overall performance characteristics. Conversely, cement-bonded particleboard manufactured from pine (Pinus caribaea M.) sawdust and coconut husk/coir (Cocos nucifera L.) demonstrated the lowest performance with a GRG value of 0.4279 (42.79%). The research methodology employed systematic normalization procedures and grey relational coefficient calculations to establish comprehensive performance rankings. Results indicate that material composition and binder selection significantly influence particleboard performance characteristics, with plastic-based resins demonstrating superior mechanical properties compared to traditional formaldehyde-based binders. This investigation provides a quantitative framework for optimizing composite particleboard manufacturing processes and material selection strategies. The findings contribute to sustainable materials engineering by identifying high-performance alternatives utilizing waste materials, thereby supporting circular economy principles in the wood products industry. The established ranking system serves as a decision-support tool for manufacturers seeking to optimize particleboard compositions for specific applications while maintaining cost-effectiveness and environmental sustainability