Membrane protein–protein interactions, essential for various cellular functions, are primarily governed by their binding affinities. Although numerous computational tools are available for predicting the binding affinity of globular protein–protein complexes, no specific tool exists for membrane protein–protein complexes. Experimental approaches to determine these affinities are costly and time-consuming, limiting large-scale applications. In this chapter, we describe MPA-Pred, a novel machine-learning-based method developed to quantitatively predict the binding affinity of membrane protein–protein complexes. We have derived both structure- and sequence-based features, as well as classified membrane proteins based on their type and function, resulting in improved performance. Extensive evaluations, including training, cross-validation, and independent test sets, demonstrate that MPA-Pred outperforms existing methods in the prediction task. The method is implemented as a user-friendly web server, freely available at https://web.iitm.ac.in/bioinfo2/MPA-Pred/ , and is built using HTML and Python, supporting recent versions of major browsers such as Chrome, Firefox, and Safari. The method serves as a valuable tool for large-scale predictions of novel membrane protein complex affinities and can aid in improving drug design strategies. Here, we overview the methodology, demonstrate the utility of our method through two case studies, and provide guidance on how to run the tool and interpret the results.