As IoT networks expand alongside advancements in 5G, accurately forecasting IoT connections becomes essential for network operators aiming to optimize infrastructure, enhance user experience, and maximize customer retention. Traditional predictive models, often limited to single-factor or historical trend analysis, struggle to capture the complex, multi-faceted influences on IoT growth, leading to suboptimal accuracy. This paper introduces a novel approach that integrates macroeconomic factors—such as GDP and digital economic indicators—into a neural network-based prediction model. By employing a backpropagation neural network (BPNN) and incorporating these macroeconomic variables as a μ-factor, the proposed model effectively captures the broader economic environment’s impact on IoT connectivity trends. Experimental results demonstrate that this hybrid model surpasses conventional techniques, providing a more nuanced and accurate forecast for IoT connections. This approach not only enhances predictive reliability but also offers a scalable framework adaptable to evolving macroeconomic conditions, paving the way for more resilient IoT infrastructure planning.