Classification of medicinal plants is critical especially in Ayurveda to identify the species that are beneficial for health. Manual classification is often time-consuming because only a few people have the expertise to identify these plants. Therefore, there is a growing need for automated systems to improve this process. In this study, we explore how machine learning can classify medicinal plants based on their unique characteristics through image processing. We utilize comprehensive information about plant images and preprocess them using techniques, such as image resizing, normalization, and color normalization (Reinhard normalization) to improve feature extraction. We use various machine learning algorithms, primarily, random forest for classification, and compare their accuracy and performance with other models. We achieve an accuracy of 76%, showing great potential for practical applications in plant research, medicine, and herbal medicine. In addition, the system designed to detect various diseases affecting plants can help reduce the success rate of Homeopathic medicine. It provides effective innovative solutions. This approach not only helps in identifying and studying beneficial plants but also in developing therapeutic options and appropriate herbal medicine techniques. The results highlight the importance of technological advancements in promoting a better understanding and use of medicinal plants.