Minimizing electrical contact resistance (ECR) at the interface between gas diffusion layer (GDL) and bipolar plate (BPP) is critical for boosting the performance and durability of proton exchange membrane fuel cells (PEMFCs). In this work, we develop and release a dedicated numerical framework that (i) stochastically reconstructs realistic carbon-fiber networks for paper‐type GDLs and then (ii) applies Greenwood-Williamson-Holm electric contact model to compute ECR under a prescribed range of external pressures. Using this, we generate a large, high‐fidelity dataset of microstructural descriptors of the fibers as well as morphological descriptors of the carbon-paper GDL— and their corresponding ECR values, spanning hundreds of stochastic GDL‐BPP configurations. We employ tree‐based ensemble regressors to learn the underlying mapping from microstructural and operating descriptors to ECR and leverage the values to rigorously rank and select a minimal subset of descriptors that account for the vast majority of ECR variance. Finally, we demonstrate that a surrogate model trained exclusively on these key descriptors predicts ECR with high accuracy and computation times that are orders of magnitude faster than that using the entire parameter space of GDL as input. Thus, this machine‐learning–continuum hybrid numerical framework not only provides fundamental insights into the physics of electrical contact at the GDL–BPP interface, but also offers a versatile, computationally highly efficient design and optimization tool for next‐generation low‐resistance PEMFC stacks.