Advanced International Journal for Research

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Ayurvedic Herbs Recommendations and Decease Recognition

Author(s) Mr. Ashish Pimpalshende, Mrs. Ayman Gani, Mr. Gitank Bhoyar, Ms.Neha Kumbhare, Mr. Atharv Jadhav
Country India
Abstract To meet the growing need for natural and reliablehealthcare options, the merging of Ayurvedic Herbs withtechno has been initiated to deliver personalized healththat is easier to access. This enterprise discloses a Full-Stack Symptom Driven Ayurvedic Herbs and Lifestyle Recommendation System empowered by machinelearning (ML). The system interprets the symptomsgiven by the user along with the personal health datasuch as age, gender, allergies, and medical history toascertain probable health conditions by means of aSupport Vector Classification (SVC) model trained onselected Ayurvedic data sets. It then suggests Ayurvedicformulations, herbal remedies, and lifestyle (diet, yoga,exercise, and daily routines) practices that arepreventative and aligned with the predicted conditionand Ayurvedic principles. The system comes with arules-based validation engine that checks forcontraindications, age-specific precautions, andingredient sensitivities, which ensures safety andrelevance. The frontend is designed as a modern, user-friendly web interface, whereas the backend constructedwith Python frameworks like Flask or FastAPI carries
out data preprocessing, disease prediction, andAyurvedic recommendation, logic apps, and self-caresupport, especially in the regions where AyurvedicHerbs is largely trusted and practiced.
Keywords Ayurvedic Herbs, Machine Learning, Symptom Analysis, Personalized Healthcare, Disease Prediction, Support Vector Classification (SVC).
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-27
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5765
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