Evidence-based personalized Ayurveda is critical for large-scale deployment and adoption of Ayurveda. The relative distribution of three doshas (prakriti) VATA, PITTA, KAPHA determines the unique phenotype of a person. Any imbalance in these doshas leads to vikriti. Currently, prakriti and vikriti determination requires deep expertise and is qualitative. To quantitatively determine the prakriti and vikriti, we have used machine learning and deep learning techniques. The prakriti prediction model is trained with 74 different phenotypic characteristics, obtained using a conversational AI-based questionnaire, on a cohort of 505 individuals. The dataset is divided into 70% training set and 30% testing set and the machine learning models are trained using Random Forest, K-Nearest Neighbor, Support Vector Machine, and Naive Bayes. Further, we developed a deep neural network to determine the vikriti (dosha imbalance) using digital nadi pariksha, carried out using smart-phone based photoplethysmography signals. The vikriti prediction model trained using DNN with linear activation and “mse” loss has a testing accuracy of 58%. In future, using more advanced machine learning and deep learning models, we aim to increase the prakriti and vikriti prediction accuracy to >90%. An Ayurveda-based digital wellness app is planned that can provide accurate diagnosis using the developed algorithms as well as personalized diet, nutraceutical, yoga, and meditation recommendations.