The Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm, providing robust security. Despite its strength, potential threats from quantum computing and decryption attempts persist. This research explores the integration of machine learning (ML) and cryptography, proposing an API-driven framework for encryption and decryption. By leveraging ML, the framework enhances security by introducing abstracted layers over encrypted data, making it interpretable only by the developed system. The API deployment aims to create a unified system, addressing vulnerabilities and demonstrating ML's effectiveness against cyber threats.