Time-honoured treatment has long relied on pharmaceutical plants as genuine remedies due to their bioactive compounds. With increasing demand for natural products and sustainable healthcare, accurately identifying and classifying these plants is crucial. However, distinguishing species is challenging due to similar physical traits and varying environmental conditions. Machine learning (ML) and deep learning (DL) have shown substantial ability in medicinal plant detection and classification by analysing large datasets and extracting subtle features. Image recognition techniques, particularly convolutional neural networks (CNNs), can identify morphological traits like leaf size, shape, and texture for classification. Studies have demonstrated that CNN models can achieve up to 90% accuracy in medicinal plant identification, enhancing the process for novel drug discovery and therapeutic applications.