A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework (PAMCs-MP) for predicting the mechanical properties of particle-reinforced aluminum matrix composites (PAMCs). Despite limited existing datasets, the approach uses extensive pre-training on larger aluminium alloy datasets to guide multi-objective optimization tasks effectively. The model achieves high predictive accuracy, R² values of over 92% for ultimate tensile strength and over 90% for elongation, demonstrating its robustness and reliability. The platform not only accelerates the design cycle but also offers profound insights into material behaviour, facilitating the development of high-strength, ductile aluminum composites tailored to specific application needs.