The increasing complexity and scale of Internet of Things (IoT) networks demand innovative approaches to resource management that ensure efficiency, reliability, and cost-effectiveness. Our proposed model tackles the increasing complexity and scale of Internet of Things (IoT) networks by leveraging multivariate regression analysis for dynamic resource allocation, enhancing system performance, reliability, and cost-efficiency. By processing real-time data from a broad range of IoT devices, the model predicts resource needs and optimizes distribution. It features several key components, data acquisition module for collecting and verifying data integrity, a preprocessing module that cleans and normalizes data for quality analysis, and a core regression analysis Engine that uses advanced statistical techniques to model relationships between input variables and resource needs. This engine adapts various regression methods, linear, logistic, or polynomial based on data characteristics to forecast demand accurately. Resource allocation is managed by a dedicated module that prioritizes tasks and dynamically adjusts resources like bandwidth according to regression outputs. A Feedback Loop continuously refines these decisions by integrating real-time adjustments, enhancing the model’s adaptability to network changes. Additionally, a user-friendly interface allows administrators to manually adjust the automated system, ensuring control over the allocation process. This robust model not only improves efficiency and reduces operational costs but also scales effectively with IoT network expansion, demonstrating significant advancements over traditional methods through extensive simulations.