This paper explores a hybrid method for predicting protein allergenicity that combines handcrafted sequence descriptors with contextual embeddings from a protein language model. In our process, AAC, DPC, and CKSAAP are derived from primary sequences and concatenated with ProtBERT representations, then input into several classifiers and three ensemble schemes. The handcrafted descriptors still contribute clear biochemical and structural cues, whereas ProtBERT adds contextual information that goes beyond local sequence patterns. In the experiments, the ensemble models behaved more steadily than any single classifier: the weighted ensemble obtained an Accuracy of 0.9310, an F1-score of 0.9309, and an AUC of 0.9825, while the stacking configuration achieved the highest AUC of 0.9834. Based on these observations, we argue that jointly exploiting compositional and contextual representations brings a concrete benefit to allergenicity prediction and offers a compact yet extensible framework for related sequence-based prediction tasks.