This paper involves designing a hybrid cryptography protocol to synthesize the application of discrete modeling in mathematics, and the security and efficiency of the next-generation cryptography protocols are optimized using deep learning. It uses Extended Residue Number Systems (ERNS) to build keys in a nonlinear way, Deep Learning Entropy Enhancer (DLEE) to augment randomness and Neural Cryptographic Optimizer Module (NCOM) to execute modular operations more quickly. Better comparison of results in experimental conditions between synthetic datasets, IoT datasets, and standardized datasets with higher entropy, faster computations and resistance of adversary applications are realized to a greater extent. The findings suggest that it is possible to provide a resilient and scalable platform to the existing cryptography systems using mathematical and flexible AI solutions.