As the next example, P301 represents a notable challenge, as the primary amine is poorly nucleophilic. This coupling is predicted to result in yields below 20%, with a confidence level of 91%. Nevertheless, this suggests the possibility that one or two ligands could yield results surpassing 20%. This example highlights the value of incorporating confidence into reaction predictions, as without it, our knowledge would be limited to the prediction of low yields for this coupling reaction. The notion that one or two ligands could potentially succeed offers a promising outlook. In fact, among the 18 tested ligands, two produced yields above 20%, thus validating both our prediction and the associated confidence levels. The recommended ligands (L21, L22, L24, L26, L31, and L35, indicated by the asterisk symbol in Fig. ) using the two nearest neighbors tactic overlooked one of the two ligands that achieved yields surpassing 20% (L27). Nonetheless, the method accurately forecasted the virtually top-performing ligand, L26. Moreover, it suggested two ligands L22 and L24 that produced yields close to the 20% threshold. This approach demonstrates significant potential as an initial step in optimization campaigns. Importantly, these two examples demonstrate the significance of the proposed workflow in mitigating risks and enhancing the likelihood of success in Ullmann C-N coupling reactions. Through the implementation of this predictive workflow, a platform that streamlines associated with reaction development has been formulated to address the inherent unpredictability associated with Ullmann C-N coupling reactions. This strategy provides a valuable supplement to traditional reaction design methods, enabling the strategic implementation of traditionally unreliable chemical methods. Through the combination of reaction exploratory assays and machine learning algorithms, this work can significantly enhance the potential for widespread adoption in the advancement of both pharmaceutical and academic interests.