In the evolving area of immunoinformatics, accurate prediction of B-cell epitopes is vital for vaccine improvement and healing interventions. This study offers a novel predictive pipeline that employs a Support Vector Machine (SVM) model optimized by way of Genetic Algorithms (GA) to decorate the accuracy and reliability of B-cellular epitope predictions. By systematically extracting key capabilities, inclusive of β-turns, antigenicity, and hydrophobicity, from peptide and protein sequences, this study applied a robust statistics preprocessing approach that consists of labeling, normalization, and dataset splitting. The performance of the proposed SVM model is carefully evaluated towards traditional methods, including Random Forest (RF) and K-Nearest Neighbors (KNN). The proposed SVM model completed an accuracy of 92.5%, a precision of 89.3%, a bear-in-mind of 91.0%, and an F1 rating of 90.1%. In comparison, the RF model obtained an accuracy of 85.0%, at the same time as the KNN version reached an accuracy of 82.5%. Visualizations, together with function importance plots, ROC curves, and confusion matrices, illustrate the model's advanced performance and its capacity for real-international packages. This study's findings underscore the importance of integrating superior machine learning strategies in immunological research and offer a complete framework for future research in epitope prediction.