Medicinal plants have been integral to healthcare since ancient times, significantly contributing to drug development and medical treatments. However, manual identification of these plants remains a labor-intensive process, limiting large-scale production and accuracy. This research proposes an automated approach for identifying medicinal plants with the help of various machine learning techniques, addressing the challenges posed by India's rich biodiversity. The system identifies plants based on their color, texture, and other features like geometric features through a three-stage process: image enhancement, feature extraction, and classification. Images of the plants, captured via smartphone cameras, are processed using digital image processing techniques to extract key features. Furthermore, the research integrates convolutional neural networks (CNNs) to enhance classification accuracy by recognizing subtle differences between species. The proposed method offers a practical and scalable solution for automating medicinal plant identification, with applications in fields such as medicine, botany, and taxonomy. Our research paper has the accuracy of 95% in comparison with previous year research papers.