Environmental Sound Classification (ESC) is an area of high interest in audio technologies, such as urban sound monitoring and intelligent surveillance solutions. The variability and dynamics of environmental sounds make it challenging to classify them using traditional models. In this work, ESC is addressed using Mel-spectrogram images and a deep twodimensional convolutional neural network (2D CNN). Initially, the input sound recordings are converted into Mel-spectrogram images. Then, a deep CNN model with three convolution blocks is designed for ten-class environmental sound classification. We have utilized batch normalization and dropout techniques to improve training stability and prevent overfitting. Further, SpecAugment and label smoothing are used for robustness and improving generalization of the proposed model. The proposed approach is evaluated on the UrbanSound8K dataset and achieves a classification accuracy of 95.19% after 50 training epochs. The experimental results demonstrate that the proposed model outperforms the existing model on the UrbanSound8K dataset.