This paper provides a comprehensive review of decision tree (DT) algorithms in machine learning, focusing on their applications, algorithms, datasets, and outcomes. Decision trees are widely used in various fields such as medical disease analysis, text classification, and image processing. The paper discusses the benefits and drawbacks of DT algorithms, including their simplicity, precision, and scalability. It also reviews recent research on DT algorithms, highlighting their performance in different datasets and applications. Key algorithms like ID3, C4.5, CART, CHAID, and QUEST are discussed, along with their impact on accuracy and efficiency. The paper concludes by summarizing the best accuracy achieved by DT algorithms, which was 99.93% using machine learning repositories. Overall, the review emphasizes the robustness and effectiveness of DT algorithms in various classification tasks.This paper provides a comprehensive review of decision tree (DT) algorithms in machine learning, focusing on their applications, algorithms, datasets, and outcomes. Decision trees are widely used in various fields such as medical disease analysis, text classification, and image processing. The paper discusses the benefits and drawbacks of DT algorithms, including their simplicity, precision, and scalability. It also reviews recent research on DT algorithms, highlighting their performance in different datasets and applications. Key algorithms like ID3, C4.5, CART, CHAID, and QUEST are discussed, along with their impact on accuracy and efficiency. The paper concludes by summarizing the best accuracy achieved by DT algorithms, which was 99.93% using machine learning repositories. Overall, the review emphasizes the robustness and effectiveness of DT algorithms in various classification tasks.