This research paper presents “Chakra Samhita,” a system that leverages machine learning to predict diseases based on user-reported symptoms and provide Ayurvedic treatment recommendations. Utilizing a Count vectorizer and a Multinomial Naive Bayes classifier, the system analyzes input symptoms to identify the most probable disease. Fuzzy matching techniques then map detected diseases to corresponding Ayurvedic treatments. Altogether, the system examines the individual’s prakriti to personalize recommendations more precisely. It also locates nearby Ayurvedic doctor, intensifying availability to professional care. A user-friendly graphical interface collects input and show results, including detailed PDF reports concluding diagnosis and recommendations. This integration of traditional Ayurvedic treatment with modern technology provides a promising appeal to preliminary disease detection and comprehensive treatment, aiming to make healthcare more available and global. Future work will focus on the expansion of data, improvising model accuracy and integrating additional healthcare services.