For decades, the prediction of protein three-dimensional structure from amino acid sequence has been a magnificent challenge problem in computational biophysics. This research topic has drawn scientists from a variety of areas of study, including biochemistry and medicine, due to its inherent scientific interest as well as the numerous potential applications for reliable protein structure prediction algorithms, ranging from genome comprehension to protein function prediction. In the past decade, there has been a significant improvement in methods for protein structure prediction and design. New data-intensive and computationally demanding approaches for structure prediction have been developed as a result of increases in computing power and the rapid growth of protein sequence and structure datasets. These approaches typically begin by assuming a probability distribution of protein structures given a target sequence and then finding the most likely structure; however, computer scientists formulate protein structure prediction as an optimization problem in finding the structural solution. Homology modeling, also known as Comparative modeling of the 3D structure of a protein by utilizing structural information from other known protein structures with good sequence similarity, is employed in our study. Homology models contain significant information about the spatial organization of key residues in the protein and are frequently employed in drug design for screening large libraries using molecular docking techniques. The generic structure prediction flowchart is followed by presentations and discussions of important concepts and techniques.