Reliable and curated experimental data form the foundation of AMP prediction models. significantly advanced this field by creating and curating the CAMP (Collection of Anti-Microbial Peptides) database. This resource was used to train three machine learning models -Discriminant Analysis (DA), Random Forest (RF), and SVM -to predict antimicrobial activity . Both RF and SVM demonstrated high performance with AUC values exceeding 0.8, whereas DA achieved a lower AUC of 0.74 . The relatively poor performance of DA likely stems from the assumption of Gaussian distribution of the underlying variables, which are inadequate for capturing the non-linear relationships typical of AMP properties and activity. SVM models, in particular, excel at AMP prediction due to their ability to effectively handle small datasets -often limited to hundreds or a few thousand sequences for AMPs -while managing high-dimensional data and non-linear relationships through diverse kernel functions. This robustness makes SVMs especially suited for the complex and multi-faceted nature of AMP activity prediction.