Abstract: The escalating interest in alternative medical practices, especially Ayurveda, has underscored the necessity for a more streamlined mechanism to recommend Ayurvedic treatments for prevalent ailments. This initiative advocates for the creation of a machine learning-driven platform that proposes Ayurvedic pharmaceuticals predicated on symptomatic presentations and diseases. The objective of this system is to amalgamate traditional Ayurvedic principles with contemporary technological advancements to deliver precise and individualized treatment suggestions. The proposed system employs a machine learning model that has been trained on an extensive dataset encompassing Ayurvedic medicinal flora, their characteristics, and their applications for an array of diseases. Through the analysis of the symptoms submitted by users, the model discerns potential diseases and endorses suitable Ayurvedic pharmaceuticals based on their efficacy in addressing the specific condition. Furthermore, the system integrates patient-centric variables, including age, gender, and medical background, to enhance the personalization of the drug recommendations. The utilization of machine learning algorithms, including classification, clustering, and regression methodologies, empowers the system to forecast the most appropriate Ayurvedic pharmaceuticals for the treatment of a designated disease. The recommendation system is constructed upon a user-centric interface, facilitating users to input their symptoms and acquire instantaneous drug recommendations accompanied by comprehensive elucidations of their therapeutic attributes. This system endeavours to reconcile the divide between traditional Ayurvedic medicine and the requirements of contemporary healthcare by furnishing accurate, evidence-informed, and personalized Ayurvedic pharmaceutical recommendations.
Keywords: Ayurvedic medicine, Herbal remedies, Natural treatment, Holistic healing, Traditional medicine, Home remedies, Plant-based medicine, Herbal formulations.
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