Proteins have a hierarchical structure that ranges from primary to quaternary, with primary structures determined by amino acid sequences and secondary structures created by interactions between amino acid side chains and polypeptide chains. Protein structure prediction is a crucial area of research in bioinformatics and computational biology due to the gap between available protein sequences and experimentally determined structures. The primary objective of this chapter is to provide a comprehensive understanding of the algorithms utilized for protein structure prediction. Recognizing the importance of accurate predictions, this chapter systematically explores the various categories of tertiary structure prediction methods, including ab initio, threading, and homology modeling. This study aims to elucidate the intricacies of these algorithms and their applications in generating precise protein structure predictions. These predictions necessitate rigorous validation, encompassing stereochemical analysis and evaluation tools like Ramachandran plots and Z scores. A notable aspect highlighted in this chapter is a case study centered on G protein-coupled receptor 3 (GPR3) model generation. This case study emphasizes the utilization of specific algorithms and underscores the significance of refinement tools in the protein structure prediction process. A comparison of the GPR3 model with other refinement tools is a valuable illustration of how different methodologies contribute to refining protein structures. The ultimate goal is to provide a more nuanced and refined protein structure that closely aligns with the experimental outcomes.