Reversible Post-Translational Modifications (PTMs) have vital roles in extending the functional diversity of proteins and effect meaningfully the regulation of protein functions in prokaryotic and eukaryotic organisms. PTMs have happened as crucial molecular regulatory mechanisms that are utilized to regulate diverse cellular processes. Nevertheless, among the most well-studied PTMs can say mainly types of proteins are containing phosphorylation and significant roles in many biological processes. Disorder in this modification can be caused by multiple diseases including neurological disorders and cancers. Therefore, it is necessary to predict the phosphorylation of target residues in an uncharacterized amino acid sequence. Most experimental techniques for predicting phosphorylation are time-consuming, costly, and error-prone. By the way, computational methods have replaced these techniques. These days, a vast amount of phosphorylation data is publicly accessible through many online databases. In this study, at first, all datasets of PTMs that include phosphorylation sites (p-sites) were comprehensively reviewed. Furthermore, we showed that there are basically two main approaches for phosphorylation prediction by machine learning: End-to-End and conventional. We gave an overview for both of them. Also, we introduced 15 important feature extraction techniques which mostly have been used for conventional machine learning methods