Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Traditional methods rely on time-consuming and labor-intensive iterative experiments to navigate a vast parameter space. To overcome this, we establish an integrated workflow merging collaborative robotics, machine learning, and finite element simulations to enable the predictive design of ultrastretchable electrodes with strain-insensitive performance. Initially, an automated pipetting robot generates 286 nanocomposites, and their electrical conductance is assessed to train a support-vector machine regressor. Through 7 active learning loops, we fabricate 146 conductive interlayers to construct an ensemble model of artificial neural networks. Leveraging the prediction model and two-scale simulations, we discover a microtextured conductive interlayer as a strain-stable platform. By evaporating a thin gold layer, we develop an ultrastretchable gold conductor with metal-like conductivity, surpassing 1,000% resistance-insensitive stretchability, and robust durability. Furthermore, electrodepositing Zn and MnO2 on gold conductors enables fabrication of a Zn//MnO2 battery showcasing >300% stretchability and strain-insensitive performance. This machine intelligence-driven approach expedites the multi-parameter optimization of stretchable electrodes, achieving strain-invariant functionalities.