Purpose of Review <p>This review examines machine learning methods for host–pathogen protein–protein interaction (PPI) prediction. The paper outlines the steps, resources, and existing algorithms used in the prediction, identifies challenges, and provides recommendations.</p> Recent Findings <p>Machine learning-based methods for host–pathogen PPI prediction typically follow a structured workflow including data collection, feature extraction, model training, and model evaluation steps. Various machine learning algorithms are used, and workflow steps are crafted in training models for PPI predictions. The model performances varied depending on the workflow steps and organisms involved.</p> Summary <p>Existing machine learning-based host-pathogen PPI prediction methods pose viable alternatives to in vivo and in vitro PPI identification. Yet, there are gaps in the research and development of these models. There are considerable opportunities for advancing tools through innovative feature extraction methods and machine learning algorithms. Moreover, there's a need for extensive experimentally validated host-pathogen PPI datasets, along with a benchmark dataset to standardize tool performance.</p>