This paper presents a comprehensive review of decision tree algorithms in machine learning, focusing on their applications, performance, and effectiveness in various domains. Decision trees are a powerful and widely used method for classification and regression tasks, offering a clear and interpretable model structure. The paper discusses different types of decision tree algorithms, including ID3, C4.5, CART, CHAID, and QUEST, and evaluates their strengths and weaknesses. It also explores key concepts such as entropy, information gain, and the use of decision trees in data mining and machine learning tasks. The paper reviews recent studies that have applied decision trees in various fields, including medical disease analysis, text classification, user smartphone classification, and image processing. It highlights the effectiveness of decision trees in achieving high accuracy and efficiency, particularly when combined with other techniques such as feature selection and dimensionality reduction. The study also discusses the benefits of decision trees, including their interpretability, efficiency, and ability to handle both numerical and categorical data. The paper evaluates the performance of decision trees in various applications, including spam detection, intrusion detection, and medical diagnosis. It compares the accuracy of decision trees with other machine learning algorithms such as KNN, SVM, and RF, and finds that decision trees often achieve high accuracy, especially when applied to well-structured datasets. The study also discusses the challenges associated with decision trees, such as overfitting and the need for careful pruning. Overall, the paper concludes that decision trees are a valuable tool in machine learning, offering a balance between accuracy, interpretability, and efficiency. They are particularly effective in applications where interpretability is important, and where the data is well-structured and manageable. The study highlights the importance of selecting the appropriate decision tree algorithm and tuning its parameters to achieve optimal performance in different applications.This paper presents a comprehensive review of decision tree algorithms in machine learning, focusing on their applications, performance, and effectiveness in various domains. Decision trees are a powerful and widely used method for classification and regression tasks, offering a clear and interpretable model structure. The paper discusses different types of decision tree algorithms, including ID3, C4.5, CART, CHAID, and QUEST, and evaluates their strengths and weaknesses. It also explores key concepts such as entropy, information gain, and the use of decision trees in data mining and machine learning tasks. The paper reviews recent studies that have applied decision trees in various fields, including medical disease analysis, text classification, user smartphone classification, and image processing. It highlights the effectiveness of decision trees in achieving high accuracy and efficiency, particularly when combined with other techniques such as feature selection and dimensionality reduction. The study also discusses the benefits of decision trees, including their interpretability, efficiency, and ability to handle both numerical and categorical data. The paper evaluates the performance of decision trees in various applications, including spam detection, intrusion detection, and medical diagnosis. It compares the accuracy of decision trees with other machine learning algorithms such as KNN, SVM, and RF, and finds that decision trees often achieve high accuracy, especially when applied to well-structured datasets. The study also discusses the challenges associated with decision trees, such as overfitting and the need for careful pruning. Overall, the paper concludes that decision trees are a valuable tool in machine learning, offering a balance between accuracy, interpretability, and efficiency. They are particularly effective in applications where interpretability is important, and where the data is well-structured and manageable. The study highlights the importance of selecting the appropriate decision tree algorithm and tuning its parameters to achieve optimal performance in different applications.