With the growing reliance on mobile communication, Short Message Service (SMS) spam has become a persistent issue, disrupting user experiences and introducing security threats such as phishing attacks and fraudulent schemes. In Vietnam, SMS spam, which frequently appears in both Vietnamese and English, exerts a negative impact on a wide range of users. To address this pervasive problem, a bilingual spam classification approach is crucial. In this paper, we propose a hybrid model, VIESpam - Vietnamese-English Spam detection- that leverages the langdetect library in Python to identify English and Vietnamese messages, followed by the application of pre-trained language models, whose embeddings are processed by a BiLSTM network for final classification. Specifically, in terms of applied pre-trained language models, to enhance adaptability to bilingual contexts, we use DistilBERT and a reduced-layer PhoBERT for English and Vietnamese messages, respectively. Experiments on a 24,086-message bilingual dataset demonstrate that VIESpam achieves competitive performance. In particular, compared to traditional deep learning models, it delivers a high accuracy of 96.75% while maintaining a streamlined and lightweight architecture. Detailed evaluations are further explored in subsequent sections.