Due to the lack of analytical solutions for the wear rates prediction of nanocomposites, we present a modified machine learning method named Dendritic Neural (DN) to predict the wear performance of copper-alumina (Cu-Al2O3) nanocomposites that have large applicability in electronics. This modification aims at determining the optimal weights of DN since they have largest influence on its performance. To achieve this improvement a new meta-heuristic technique named Artificial Hummingbird Algorithm (AHA) was used. The modified model was applied to predict the wear rates and coefficient of friction of Cu-Al2O3 nanocomposites that was developed in this study. Electroless coating of Al2O3 nanoparticles with silver (Ag) was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical and wear properties of the produced composites with different Al2O3 content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear test at different load and speeds. The developed model using AHA algorithm showed excellent predictability of the wear rate and coefficient of friction for Cu-Al2O3 nanocomposites with reinforcement content up to 10%.