A method for prioritizing and contextualizing user data on a user equipment, includes: identifying a plurality of parameters associated with user behavior by analyzing at least one of entities, relations, and contexts in structured user data using a machine learning (ML) model; identifying at least one behavioral pattern of the user behavior based on the plurality of parameters; mapping the at least one behavioral pattern to the contexts; prioritizing the entities and the relations based on the mapped at least one behavioral pattern; predicting, using the ML model, a plurality of chains of thought based on priority of the entities and the relations; identifying a chain of thought having a highest probability of occurrence, among the plurality of chains of thought, by comparing the plurality of chains of thought using the ML model; and prioritizing and contextualizing the user data based on the chain of thought having the highest probability of occurrence.