To address these challenges, researchers have increasingly turned to machine learning (ML) models to enhance reaction yield predictions and streamline the selection of optimal reaction conditions. Recent advancements highlight the transformative impact of ML in this domain. Notably, Doyle et al. pioneered the use of Random Forest algorithms in predicting yields for C-N cross-coupling reactions, showcasing ML's capability to significantly reduce experimental workloads and expedite the discovery process. Building on this foundation, the Sigman and Denmark groups have extended these predictive models to encompass broader reaction scopes, thereby increasing their robustness and applicability across diverse chemical landscapes. These efforts represent a substantial leap forward in the practical deployment of ML, moving beyond proof-of-concept to tools that offer real-world utility. Schwaller et al. introduced an approach using Bidirectional Encoder Representations from Transformers (BERT) neural networks to predict yield based on textual descriptions of chemical reactions, which leverages natural language processing (NLP) algorithms to interpret reaction transformation outcomes. The first amide coupling reaction yield prediction model by Isayev et al. utilized literaturebased reactions curated from Reaxys 15 to build predictive models but highlighted the inherent difficulties of using such data. Literature reactions often suffer from inconsistencies in reporting, variability in experimental conditions, and a lack of comprehensive datasets, making it challenging to build robust and generalizable models. Additionally, literature sources typically report only successful reactions with high yields, neglecting low-yield and negative data that are crucial for creating well-distributed and accurate predictive models. It is essential to curate relevant datasets for model development and to identify and control factors that complicate yield prediction. The variability in data sources, reaction scales, and structural diversity reported in the literature further complicates the development of reliable models. Sigman et al. explored a related challenge in predicting reaction rates for amide coupling using linear free-energy relationships, emphasizing the importance of understanding underlying reactivity trends. Although their work focused on rates rather than yields, the overarching goal of providing actionable insights for the synthetic community is similar to our presented work. Very recently, Doyle et al. further explored the optimization of reaction conditions through bandit optimization technique to efficiently navigate the vast chemical space, balancing the exploration of new conditions with the exploitation of known successful ones. This approach is particularly powerful for the case study amide coupling reaction, where multiple variables-such as reagent, solvent, and temperature-must be optimized simultaneously. By using bandit optimization, the researchers were able to significantly reduce the number of experimental trials needed to identify optimal conditions, showcasing yet another example of how ML can accelerate and enhance the process of chemical discovery.