The use of image analysis to identify medicinal plants not only advances botanical research but also helps preserve knowledge about herbal remedies. In this study, we used the EfficientNet architecture, known for its efficiency and accuracy due to its scalable model dimensions, to classify images of various medicinal plants. The goal was to develop a deep learning model that could achieve high accuracy even with a relatively small dataset, which is common in specialized botanical categories. We fine-tuned the EfficientNet model to process images captured in various environmental conditions, making the model more robust and generalizable. The dataset included several hundred images for each plant category, ensuring that the model learned detailed features crucial for accurate classification. After rigorous training and validation, our EfficientNet-based model achieved impressive precision, significantly outperforming traditional convolutional neural networks in terms of accuracy, with a notable reduction in computational resources. The ultimate model showcased an overall accuracy of 96.87% on a separate test set, affirming the effectiveness of employing sophisticated, scalable structures for botanical categorization tasks. We utilized precision, recall, and F1-score metrics to evaluate the model’s performance, demonstrating its potential in practical scenarios that demand swift and dependable plant recognition. This methodology sets the stage for further exploration into utilizing advanced deep-learning methods in identifying medicinal plants.