As the field of computational catalysis continues to evolve, recent advances leverage emerging AI machinery and large data to understand and predict catalytic behavior. In this context, this work introduces AIMNet2-Pd, a machine learning interatomic potential, that enables rapid 3D mechanistic modeling of Pd-catalyzed Suzuki-Miyaura cross-coupling reactions. To the best of our knowledge, our model represents the first successful application of MLIPs to accurately simulate chemical reactions involving both organic and metal-containing molecules. Instead of requiring days of supercomputer time for traditional QM calculations, AIMNet2-Pd performs these calculations in minutes on a standard desktop computer, making advanced computational studies of organometallic catalysts more accessible to the broader chemical community.