Machine learning and deep learning techniques have appeared as powerful tools for predicting PPIs. These approaches use the vast amounts of biological data available, ranging from genomic and proteomic sequences to structural information, to infer and anticipate protein interactions. This research paper embarks on a comprehensive exploration of the application of both traditional machine learning algorithms and advanced deep learning architectures for PPI prediction.