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E-Payment has become popular in mobile com-merce, can provide consumers with a convenient way to makepurchases electronically. Currently, however, all too many E-Payment systems are primarily focused on securing a consumer’sfinancial information and do little to prevent privacy leaks andAI-generated scams. This paper defines AEP-M, a novel AI-enhanced anonymous e-payment scheme developed for mobiledevices that uses TrustZone and divisible e-cash. Since mobiledevices have very limited processing power and each transactionmust be performed in real time, the proposed solution combinesan efficient divisible e-cash system with AI-powered anomalydetection techniques to improve both the security, privacy andfraud detection in mobile payments. In addition to enablingusers to divide a single withdrawal of an e-coin of a largeamount into multiple transactions without disclosing their iden-tity to either banks or merchants, AEP-M integrates AI-basedrisk assessment to identify suspicious spending behaviors torapidly mitigate fraud and continuously monitor transactions.By employing a combination of bit decomposition and pre-computation to minimize the computational overhead of thetransaction process, AEP-M provides the optimal performancein terms of minimizing the max number of exponentiationoperations required to perform the frequent online spendingprocess on elliptic curves. Finally, AEP-M also incorporates anARM TrustZone to protect a user’s financial data and importantprivate data; an SRAM PUF is used as a Root of Trust to deriveAI-powered keys and manage sensitive data, thereby increasingboth the security and reliability of the system. A prototype ofAEP-M was implemented and evaluated using the BN curve ata 128-bit security level. The experimental results demonstratedthat AEP-M is capable of improving the Security, Efficiency andFraud Detection capabilities of Mobile Digital Payments whilemaintaining User Privacy and Anonymity.