Machine learning (ML) is increasingly used in vaccine design to identify B and T cell epitopes and correlate protection. ML models help predict epitopes and their immunogenicity, improving vaccine design by providing insights into immune responses. These models are trained on various data types, including sequence and structure information, and can be supervised or unsupervised. Recent advances in ML have enabled more accurate predictions of epitope-paratope interactions, which are crucial for vaccine development. ML also aids in understanding the molecular processes underlying immune responses, offering new tools for scientific discovery. However, challenges remain in data availability and model development to translate ML predictions into practical vaccine design. ML methods, such as feed-forward neural networks, restricted Boltzmann machines, and convolutional neural networks, are used for epitope prediction and antigen presentation. These models can predict binding affinities and help identify immunogenic epitopes. Additionally, interpretable ML approaches, such as decision trees and attention mechanisms, provide insights into model predictions, enhancing the biological understanding of epitope recognition. Despite progress, challenges remain in accurately predicting epitope immunogenicity and epitope-paratope interactions, particularly for less characterized HLA alleles. Overall, ML is a powerful tool for rational vaccine design, offering new insights and improving the efficiency of vaccine development.Machine learning (ML) is increasingly used in vaccine design to identify B and T cell epitopes and correlate protection. ML models help predict epitopes and their immunogenicity, improving vaccine design by providing insights into immune responses. These models are trained on various data types, including sequence and structure information, and can be supervised or unsupervised. Recent advances in ML have enabled more accurate predictions of epitope-paratope interactions, which are crucial for vaccine development. ML also aids in understanding the molecular processes underlying immune responses, offering new tools for scientific discovery. However, challenges remain in data availability and model development to translate ML predictions into practical vaccine design. ML methods, such as feed-forward neural networks, restricted Boltzmann machines, and convolutional neural networks, are used for epitope prediction and antigen presentation. These models can predict binding affinities and help identify immunogenic epitopes. Additionally, interpretable ML approaches, such as decision trees and attention mechanisms, provide insights into model predictions, enhancing the biological understanding of epitope recognition. Despite progress, challenges remain in accurately predicting epitope immunogenicity and epitope-paratope interactions, particularly for less characterized HLA alleles. Overall, ML is a powerful tool for rational vaccine design, offering new insights and improving the efficiency of vaccine development.