Continuous progress in cross-coupling technology is largely attributed to the discovery of new ligand and catalyst species. A key challenge in metal-catalyzed synthetic chemistry is achieving immediate, high-fidelity predictions of reaction outcomes. This is especially important considering the pace of progress in laboratory automation platforms and high-throughput experimental methods. Integrating molecular modeling, experimental datasets, and ML offers an ideal solution, as it potentially minimizes both human and financial resource costs associated with traditional design of novel catalytic synthesis routes that employs trial-and-error reaction optimization via experimentation. AIMNet2-Pd represents a significant advancement in MLIPs, offering a drop-in replacement for costly DFT calculations in organometallic chemistry, particularly with its ability to simulate on-the-fly reactive phenomena. The model provides accurate predictions of reaction thermodynamics, transition states, and full reaction pathways for palladium-catalyzed cross-couplings while maintaining accuracy comparable to DFT. The model's unique ability to predict partial atomic charge distributions enables deeper rationalization and analysis of observed trends. Altogether, AIMNet2-Pd framework is essential for tackling persistent challenges in Pd-catalyst design, including systematic screening of ligand effects on activity and selectivity, analysis of competing reaction pathways and side reactions, optimization of stereochemical control, and rapid evaluation of novel catalyst designs.