Herein, we report a transferable reactive machine learned interatomic potential (MLIP) based on 2nd generation of our atoms-in-molecules (AIMNet2) neural network architecture. The AIMNet2-Pd is designed for rapid calculations of the structure and thermodynamics of intermediates and transition states in Pd-catalyzed cross-coupling reactions. This model represents progress toward the underexplored area of general hybrid organic-inorganic MLIPs, especially for reactive chemistry. Crucially, AIMNet2-Pd enables accurate mechanistic modeling of Pdcatalyzed couplings without resorting to computationally expensive electronic structure calculations, offering support to move high-throughput computational catalyst screening from campaigns on large supercomputers to a less demanding task that can be managed locally with modest resources. Our MLIP achieves this by learning from several million DFT calculations that intentionally target broad chemical space coverage of equilibrium and off-equilibrium structures as well as conformers along minimum energy pathways connecting reactants, products, intermediates, and transition states. Specifically, by focusing on C-C coupling, we demonstrate that intermediate structures and key catalytic mechanisms (e.g., oxidative addition, transmetalation, and reductive elimination ) can be readily characterized by probing the AIMNet2-Pd potential energy surface. Consequently, comparative ligand analysis, i.e., catalyst screening studies, can be carried out at all the main steps in the Suzuki-Miyaura cycle without retraining. As a final point, we examine the broader generalizability of our model on out-ofdistribution molecular catalysts, e.g., a dinuclear Pd compound.