Precise prediction of peptide binding with major histocompatibility complex (MHC) molecules is crucial for comprehending immune responses, discovering epitopes, and designing vaccines. Over time, many machine learning algorithms have been created to aid in the prediction of MHC–peptide binding. These techniques vary from simple approaches that only consider the amino acid sequences of the peptides to more complex systems that also incorporate positional information. This chapter gives a summary of current breakthroughs in MHC–peptide binding prediction tools, clarifying the varied array of machine learning algorithms applied. Through a critical analysis of these methods, this study identifies the difficulties involved in accurately predicting binding and highlights the specific areas that require additional research and development. This review seeks to stimulate progress in the field of MHC–peptide binding prediction by analyzing the current state of the field and proposing future directions. The ultimate goal is to improve our understanding of immune responses and facilitate breakthroughs in epitope identification and vaccine design.