The continuing development of secure communication is analyzed through the perspective of neural network-based cryptography, with a focus on its ability to improve authentication, key generation, encryption, and decryption. This research investigates improvements that occurred in the last five years, to unveil machine learning's revolutionary role in data security. This analysis goes over the technical protocols of key generation and exchange to the novel strategies used in encryption schemes, including the merging of neural networks with steganography for covert communication. Furthermore, it explores the symbiotic relationship between homomorphic encryption and machine learning, revealing the ways in which these techniques jointly contribute to securing data while preserving computational utility. Additionally, this study covers the integration of machine learning in intrusion detection systems, offering insights into how these systems are stimulated to defend against evolving threats. This comprehensive review analyses to uncover novel techniques and current patterns, demonstrating machine learning's transformational impact on improving data security procedures.