This review article provides a comprehensive overview of common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). The authors discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees using R packages and algorithms. The article aims to provide researchers with a reference for selecting the most appropriate method for their specific research questions and datasets. It also highlights the importance of integrating advanced computational methods and machine learning technologies to handle large datasets and improve the accuracy and efficiency of phylogenetic tree construction. The review concludes by discussing emerging methods such as deep learning-based approaches and hyperbolic embedding, emphasizing the need for continued research and innovation in phylogenetic analysis.This review article provides a comprehensive overview of common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). The authors discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees using R packages and algorithms. The article aims to provide researchers with a reference for selecting the most appropriate method for their specific research questions and datasets. It also highlights the importance of integrating advanced computational methods and machine learning technologies to handle large datasets and improve the accuracy and efficiency of phylogenetic tree construction. The review concludes by discussing emerging methods such as deep learning-based approaches and hyperbolic embedding, emphasizing the need for continued research and innovation in phylogenetic analysis.