Improving environmental sound classification is crucial for enhancing intelligent surveillance systems. In this study, we propose a hybrid approach that integrates data augmentation techniques with a deep learning architecture combining a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), namely Hybrid Deep Learning–Data Augmentation (DLDA). The CNN (specialized in extracting local spatial features from spectrograms) captures discriminative patterns in the time–frequency domain, while the BLSTM (a recurrent neural network capable of learning temporal dependencies in both forward and backward directions) models long-range contextual information in audio sequences. To improve model generalization, we apply multiple data augmentation methods, including Gaussian noise addition, time shift, time stretching, pitch shifting, loudness scaling, and time delay. Experiments on the ESC-50 dataset demonstrate that our method achieves significantly higher classification accuracy compared to recent studies, confirming the effectiveness of combining advanced augmentation with our modeling.