The growing complexity of cyber attacks has necessitated the evolution of firewall technologies from static models to adaptive, machine learning-driven systems. This research introduces "Dynamically Retrainable Firewalls", which respond to emerging threats in real-time. Unlike traditional firewalls that rely on static rules to inspect traffic, these advanced systems leverage machine learning algorithms to analyze network traffic pattern dynamically and identify threats. The study explores architectures such as micro-services and distributed systems for real-time adaptability, data sources for model retraining, and dynamic threat identification through reinforcement and continual learning. It also discusses strategies to improve performance, reduce latency, optimize resource utilization, and address integration issues with present-day concepts such as Zero Trust and mixed environments. By critically assessing the literature, analyzing case studies, and elucidating areas of future research, this work suggests dynamically retrainable firewalls as a more robust form of network security. Additionally, it considers emerging trends such as advancements in AI and quantum computing, ethical issues, and other regulatory questions surrounding future AI systems. These findings provide valuable information on the future state of adaptive cyber security, focusing on the need for proactive and adaptive measures that counter cyber threats that continue to evolve.