This review discusses machine learning (ML) methods for cancer characterization from microbiome data, emphasizing the importance of sample collection, feature selection, and model validation. The microbiome, consisting of bacteria, viruses, and fungi, has been shown to influence cancer development, progression, and treatment response. Next-generation sequencing technologies have enabled detailed microbiome analysis, revealing cancer-specific microbial signatures. ML models are essential for analyzing complex microbiome data, as they can uncover patterns that are difficult for humans to detect. However, many ML studies have reported conflicting results due to poor generalizability of models. The review highlights the need for improved model validation, the use of expanded datasets, and the exploration of alternative microbiome representations beyond taxonomic profiles. It also discusses the limitations of current ML approaches and suggests future directions, such as leveraging deep learning and developing models better suited to microbiome data. The review covers various ML methods, including Support Vector Machines (SVMs), Decision Tree-based models like Random Forests, Logistic Regression, and Artificial Neural Networks (ANNs). While SVMs and Random Forests have shown promise, their performance varies depending on the dataset and task. Logistic Regression is useful for feature selection and benchmarking, while ANNs can achieve better performance but are difficult to interpret. The review concludes that further research is needed to improve the accuracy, generalizability, and interpretability of ML models for cancer characterization from microbiome data.This review discusses machine learning (ML) methods for cancer characterization from microbiome data, emphasizing the importance of sample collection, feature selection, and model validation. The microbiome, consisting of bacteria, viruses, and fungi, has been shown to influence cancer development, progression, and treatment response. Next-generation sequencing technologies have enabled detailed microbiome analysis, revealing cancer-specific microbial signatures. ML models are essential for analyzing complex microbiome data, as they can uncover patterns that are difficult for humans to detect. However, many ML studies have reported conflicting results due to poor generalizability of models. The review highlights the need for improved model validation, the use of expanded datasets, and the exploration of alternative microbiome representations beyond taxonomic profiles. It also discusses the limitations of current ML approaches and suggests future directions, such as leveraging deep learning and developing models better suited to microbiome data. The review covers various ML methods, including Support Vector Machines (SVMs), Decision Tree-based models like Random Forests, Logistic Regression, and Artificial Neural Networks (ANNs). While SVMs and Random Forests have shown promise, their performance varies depending on the dataset and task. Logistic Regression is useful for feature selection and benchmarking, while ANNs can achieve better performance but are difficult to interpret. The review concludes that further research is needed to improve the accuracy, generalizability, and interpretability of ML models for cancer characterization from microbiome data.