The discovery and use of medicinal plants are essential for both conventional and modern health systems. This study introduces a unique deep learning technique using EfficientNetB3 models for the detection and extraction of medicinal plants. For the chemical models, the version is trained on specialized datasets, including various plant species, to ensure classification accuracy. Through deep learning, this proposed technique provides a reliable and efficient solution for identifying medicinal plants based on specific characteristics. The EfficientNetB3 model demonstrates better overall performance in classification tasks, even with limited computing resources. The application of deep learning in plant chemical identification holds promise in fields such as medicine, ethnobotany, and conservation biology, enabling researchers, health professionals, and enthusiasts to quickly catalog medicinal plants and gain insights into their healing properties. In particular, the EfficientNetB3 model facilitates the efficient identification and classification of medicinal plants, thereby advancing plant research and improving health practices.