This study explores using neural networks to enhance cryptography applications by leveraging their inherent capacity to learn and adapt to specific configurations derived from input datasets. Specifically, the focus is on addressing the computational demands associated with Elliptic Curve Cryptography, a cryptographic method entailing intricate mathematical computations that traditionally necessitate substantial computing resources. Our research introduces a novel approach wherein neural networks are employed to efficiently compute the operations integral to the encryption and decryption processes within Elliptic Curve Cryptography. This offers a promising avenue for optimizing cryptographic algorithms.