Design of Experiments (DoE) is increasingly reshaping how nanomaterials are discovered, optimized, and understood, enabling a shift from empirical trial-and-error toward predictive, knowledge-driven design. As nanotechnology advances toward multifunctional and highly coupled systems, unstructured experimentation struggles to deliver reproducibility, efficiency, or transferability. This perspective highlights the evolution of DoE from classical factorial designs and response surface methodology to Bayesian, adaptive, and machine-learning-enabled frameworks. We discuss how structured experimentation reveals hidden interactions, supports multi-objective optimization, and enables uncertainty-aware decision-making across complex synthesis spaces.