Currently, malware activities pose a substantial risk to the security of Android applications. These risks are capable of stealing important information and causing chaos in the economy, social structure, and financial sector. Malicious network traffic targets Android applications due to their constant connectivity. This study develops the NMal-Droid approach for network-based Android malware detection and classification. First, we designed a packet parser algorithm that filters the combination of HTTP traces and TCP flows from PCAPs (Packet Capturing) files. Second, the fine-tune embedding approach is developed that uses a word2vec pre-trained model to analyze features’ embeddings in three different ways, i.e., random, static, and dynamic. It is used to learn and extract feature-matrix matrices with related meanings. Third, The Convolutional Neural Network (CNN) is used to extract effective features from embedded information. Fourth, the Bi-directional Gated Recurrent Unit (Bi-GRU) neural network is designed to compute gradient computation in the context of time-forward and time-reversed. Finally, a multi-head ensemble of CNN-BiGRU is developed for accurate malware classification and detection. The proposed approach is evaluated on five different activation functions with 100 filters and a range of 1–5 kernel sizes for in-depth investigation. An explainable AI-based experiment is conducted to interpret and validate the proposed approach. The proposed method is tested using two big Android malware datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2k malware and 3.2K benign samples. It is shown that the proposed approach outperforms as compared to the state-of-the-art methods.