Abstract: Systems and methods for digital wallet reward optimization using reverse-engineering and machine learning models include determining that a transaction is at a checkout and obtaining digital wallet information. A first machine learning model classifies the transaction as able to produce a potential reward for a set of cards provided in the digital wallet information and predicts a first reward amount for each card. A second machine learning model, that has extracted campaign definitions for each of the cards, classifies the transaction as able to produce a potential reward for each of the cards based on the extracted campaign definitions and predicts a second reward amount for each of the cards. A third machine learning model evaluates the first and second reward amounts to determine a final predicted reward amount for each card of the set of cards. A card having the greatest reward amount may be recommended to the user.