The rapid evolution of digital threats is a major hurdle to the security of vital infrastructure, driving the need for advanced cybersecurity methods like those based on the use of new technologies. This research seeks to assess the use of neural networks in cybersecurity and especially the role of these technologies in proactive risk management and threat intelligence. Neural networks, mainly deep learning models, had excellent success in detecting, analyzing, and mitigating all cyber threats with no time delay. Through the integration of sophisticated components such as pattern recognition, anomaly detection, and predictive analytics, these models improve threat detection accuracy while minimizing false positives. The review of the latest neural network models applied in cyber security presented in this publication is the greatest, for example convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, with a full analysis of how effective they are in protecting critical infrastructure. Furthermore, the connection of neural networks with security information and event management (SIEM) systems enabling automated threat responses and the continuous learning of evolving cyberattack patterns is also included in this paper. Some challenges are also addressed like adversarial machine learning, data privacy concern, and the implementation of Computational efficiency. It concludes by finding that the possibilities of using neural networks to increase cybersecurity resilience are remarkable and that such systems could be optimized in the future with AI for a solid, robust, and adaptive strategy for critical infrastructure protection.