Deciphering the complex molecular processes in living creatures requires an understanding of the three-dimensional structure of proteins. The development of computational methods to predict protein structures was prompted by the time and resource constraints associated with experimental methods for discovering protein structures. This work investigates the developments in the prediction of protein structures using an extensive computational framework. We review the diverse techniques employed in the field, ranging from ab initio methods to homology modeling, and discuss the integration of machine learning algorithms for enhanced accuracy. The challenges associated with predicting protein structures, such as the vast conformational space and the accurate representation of protein–ligand interactions, are addressed. Additionally, we highlight the impact of recent advancements in bioinformatics and structural biology on improving prediction methodologies. This chapter offers a thorough summary of the computational techniques used to predict protein structures, highlighting their importance in solving molecular puzzles. Combining computational techniques with experimental data has the potential to improve our knowledge of protein function and pave the way for innovations in a range of biomedical research areas.