This review summarizes common methods for constructing phylogenetic trees and their implementation in R. Phylogenetic trees reflect evolutionary relationships between species or gene families and are crucial in modern biological research. The article discusses distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree), highlighting their advantages, shortcomings, and applications. It provides R code for constructing phylogenetic trees from molecular data using packages and algorithms in R. The review aims to offer comprehensive guidance for researchers seeking to construct phylogenetic trees and promote further development in this field. It also presents a clear overview of different methods to help researchers select the most appropriate approach for their specific research questions and datasets. The review includes detailed descriptions of various phylogenetic tree construction methods, their implementation in R, and examples of code for constructing phylogenetic trees using different methods. It also discusses advanced computational integrative methods for inferring phylogenetic trees, such as concatenation phylogeny and coalescence phylogeny. The review concludes with a summary and perspectives on the future of phylogenetic tree construction, emphasizing the importance of developing new methods and integrating artificial intelligence and machine learning technologies into phylogenetic tree construction. The review also highlights the use of R as a powerful statistical analysis and plotting tool, providing many packages for constructing and analyzing phylogenetic trees. The review includes a table of common R packages used in phylogenetic tree construction and provides code examples for implementing various phylogenetic tree construction methods in R.This review summarizes common methods for constructing phylogenetic trees and their implementation in R. Phylogenetic trees reflect evolutionary relationships between species or gene families and are crucial in modern biological research. The article discusses distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree), highlighting their advantages, shortcomings, and applications. It provides R code for constructing phylogenetic trees from molecular data using packages and algorithms in R. The review aims to offer comprehensive guidance for researchers seeking to construct phylogenetic trees and promote further development in this field. It also presents a clear overview of different methods to help researchers select the most appropriate approach for their specific research questions and datasets. The review includes detailed descriptions of various phylogenetic tree construction methods, their implementation in R, and examples of code for constructing phylogenetic trees using different methods. It also discusses advanced computational integrative methods for inferring phylogenetic trees, such as concatenation phylogeny and coalescence phylogeny. The review concludes with a summary and perspectives on the future of phylogenetic tree construction, emphasizing the importance of developing new methods and integrating artificial intelligence and machine learning technologies into phylogenetic tree construction. The review also highlights the use of R as a powerful statistical analysis and plotting tool, providing many packages for constructing and analyzing phylogenetic trees. The review includes a table of common R packages used in phylogenetic tree construction and provides code examples for implementing various phylogenetic tree construction methods in R.