<p>Methane is an explosive and dangerous gas that requires quick and reliable safety detection in industries and homes. This research work proposes a Design of Experiment (DOE)–Grey Relational analysis (GRA)-based statistical optimization framework for microstructural optimization of Co-doped ZnO thin films for enhanced methane-sensing performance. A sol–gel spin-coating method was used to prepare the Co-doped ZnO films, and the influence of cobalt concentration, molarity of the precursors, and annealing temperature was investigated in a systematic manner with a Taguchi-based design of experiments. FESEM, XRD, EDAX, and AFM were used to identify microstructural features and the key performance indicators that were identified, such as the grain size and the surface roughness. GRA was employed to establish correlations between fabrication parameters and microstructural responses, enabling identification of the optimum processing conditions. The optimized films have fine grains, homogeneous porosity, and moderate roughness. Methane sensing shows that it was sensitive, quick to respond, and quick to recover. Additionally, the films exhibited excellent repeatability and maintained a stable baseline. The results showed a strong relationship between grey relational grade and methane sensing performance, making it an effective method for statistical microstructural optimization in the design of advanced gas sensors.</p>