Accurate IoT device classification is essential for secure and efficient network management in heterogeneous environments. However, existing approaches struggle with overlapping traffic patterns and limited generalization across dynamic device behaviours. This paper proposes a Metadata Fusion with Attention-based BiLSTM (MFA-BiLSTM) framework that integrates time-domain and statistical features using an adaptive attention mechanism. The model captures both sequential dependencies and distributional characteristics of network traffic, enhancing feature representation and classification robustness. Experiments conducted on the CIC-IoT-Dataset2022 demonstrate improved performance with an accuracy of 99.40%, precision of 99.39%, recall of 99.36%, and F1-score of 99.38%. The results indicate that the proposed approach achieves reliable and scalable IoT device classification while maintaining interpretability.