Identifying medicinal plants is important for their use in medicine and other areas. Plants can be recognized with the help of leaves and their parameters, like margin, texture, shape, etc. Laboratory-based testing involves lengthy procedures as well as the need for expertise with sample handling and data interpretation. This has to do with image processing and machine learning. Hence, this study highlights advancements in technology, particularly in image processing and machine learning, and explores the use of texture and shape features in classifying plants using classifiers like CNN, KNN, ANN, SVM, and PNN. The implementation shows the practical use of the ResNet50 model for the precise identification of medicinal plants. Using the Indian Medicinal Plant Leaf Image Dataset, which has 6900 images in 80 classes, this implementation uses the ResNet50 model to accurately identify medicinal plants. After preprocessing, training, and assessment on a separate test dataset, our model yielded an impressive accuracy of 87.43%.