he Resource Allocation Optimizer is an innovative AI-driven solution designed to address the challenges of bandwidth allocation in public sector networks, particularly in underserved regions. Public sector connectivity networks often face issues like resource scarcity, suboptimal prioritization, and inefficiencies in service delivery for critical sectors such as healthcare, education, and emergency services. This system leverages the power of Groq's Llama API to analyze incoming service requests and assign numerical priority scores based on urgency, bandwidth requirements, and the nature of the service. These prioritized requests are then processed by a dynamic resource allocation algorithm that distributes available bandwidth in real-time, ensuring that high-priority services receive the resources they need without delays.Key features of the project include: Simulated Environment: A network simulation generates realistic service requests for bandwidth from sectors like healthcare, education, and emergency services. AI-Powered Decision Making: The Groq Llama model analyzes requests and assigns priorities, enabling fast and accurate resource allocation. Dynamic Resource Allocation: The allocation algorithm ensures bandwidth is distributed efficiently, balancing critical needs and overall network availability. Interactive Dashboard: Built using Streamlit, the dashboard visualizes service logs, priorities, and bandwidth usage with real-time updates and dynamic charts, offering transparency and user engagement. This project has immense value in enhancing the efficiency of public sector networks by automating resource management, improving service outcomes, and reducing operational overhead. It is designed with scalability in mind, allowing integration with real-world networks and enabling further customization for specific scenarios.