This work aims to delve into the complex world of protein structure discovery by investigating the relationship between molecule type, experiment technique, residue count, and resolution. By using state-of-the-art machine learning techniques such as K-means clustering, Hierarchical clustering, and DBSCAN, this research seeks to uncover the underlying connections between crystallographic data and various factors that impact the process of solving protein structures. The results of this study could lead to a deeper understanding of the key factors that contribute to successful protein structure discovery and pave the way for future advancements in the field.