Wood is an important raw material used in various activities, such as building, furniture, and fuel. The timber industry is significant in many countries and has a significant financial impact. There are diverse categories of wood, each with its unique properties, and experts typically perform wood species identification through visual inspection, which is a tedious and time-consuming process. To eliminate the need for manual detection, a deep learning-based wood Classifica-tion System was proposed in this paper. The system uses a transfer learn-ing-based convolutional neural network model that handles feature extraction. Compared to other transfer learning models such as VGG16, ResNet50, and DenseNet201, the proposed EfficientNetB7 model achieved a high validation accuracy of 99.824%, which suggests that it can be used to aid unskilled agents in wood categorization. This new strategy can save time and effort in the identi-fication of wood species, making it an efficient method for the timber industry.