High quality processing key to improving product quality and enterprise benefits. In this work, an adaptive network–based fuzzy inference system (ANFIS) was combined with milling experiments to understand the effects of tool geometry and milling parameters on the surface quality of wood fibre–reinforced magnesium oxide composite (WRMC). Specifically, changes in surface roughness (Ra) and damage of WRMC at different milling conditions were assessed using ANFIS and micro-analysis methods. Development of ANFIS models were confirmed to be reliable for predicting surface roughness. Changes in surface roughness at different milling conditions were determined, and the lowest surface roughness was obtained at the highest rake angle, highest cutting speed, and smallest milling depth. Furthermore, pitting-type damage irregularly distributed on the machined surface is attributed to the pulling out and debonding of wood fibres. Overall, high cutting speed, shallow cutting depth, and high rake angle is recommended for fine machining of WRMC where a smooth surface is desired. This study showcases how neuro-fuzzy models can be combined with conventional micro-analysis to optimize milling parameters for WRMC to minimize surface damage, and paves the way for future studies to optimize cutting tool life and energy consumption.