This paper presents an adaptive queuing model with entropy-based priority scheduling, designed to improve packet processing efficiency and security in Internet of Things (IoT) environments. The proposed model employs an entropy-based prioritization mechanism to classify packets by their unpredictability, assigning high-priority status to data with higher entropy-such as encrypted or time-sensitive packets. Additionally, the model integrates dynamically adjusted polling intervals, enabling it to respond to fluctuations in traffic load by shortening intervals when queues lengthen and lengthening them under lighter loads. This adaptability enhances overall throughput and reduces latency, ensuring timely processing even under heavy network demands. To improve security, the system utilizes randomized polling intervals, reducing the predictability of packet processing and mitigating risks of timing attacks. The model was tested through simulations, demonstrating stable queue lengths, reduced wait times for high-priority packets, and consistent throughput across varied load conditions. Results indicate that this queuing system is well-suited for IoT applications with diverse data types and unpredictable traffic patterns. Potential improvements are discussed, including lightweight entropy calculations, predictive adjustments using machine learning, and energy-efficient scheduling strategies, aiming to enhance the model’s adaptability for resource-constrained IoT devices.