A key component of drug design and discovery is protein-ligand binding prediction, which helps to create new therapeutic approaches with higher specificity and effectiveness. In this endeavour, computational approaches have proven to be invaluable tools, providing insights into the sophisticated chemical interactions among proteins and ligands. This chapter presents a thorough review of the methods, techniques, and applications of the computational algorithms used to predict protein binding. An overview of the importance of protein-ligand interaction in drug development and the difficulties involved in experimentally determining binding affinities opens the chapter. The chapter covers about number of methods, including solvation effects, protein flexibility, and scoring function optimisation, that may be used to improve the precision and dependability of protein binding predictions, and also demonstrating the usefulness of computational techniques in predicting the bind modes, affinities ranking, structure-activity relationship, examples and case studies. Additionally, the chapter examines contemporary developments in the area, such as the incorporation of massive amounts of data for large-scale virtual screening, quantum physics/molecular mechanics (QM/MM) modelling, and multi-scale modelling methodologies. This chapter emphasizes about how crucial computational techniques are hastening the drug development process by making it easier to rationally designed ligands with the necessary binding characteristics.