The Internet of Things (IoT) has become a cornerstone of modern technological advancements, providing ubiquitous connectivity among devices across various domains. However, the communication reliability within IoT networks continues to present significant challenges due to dynamic environmental conditions, network congestion, and varying device capabilities. This paper explores the role of machine learning (ML) in enhancing communication reliability within IoT networks. By employing advanced ML techniques, such as predictive modeling, anomaly detection, and adaptive routing, it is possible to address common communication issues such as packet loss, latency, and congestion. Through simulation and analysis, this study demonstrates that ML-based strategies significantly improve network reliability, ensuring more stable and efficient communication in real-time. The findings underscore the potential of integrating machine learning algorithms into IoT communication protocols to achieve better performance, scalability, and fault tolerance in diverse application scenarios.

Internet of Things (IoT), Machine Learning, Communication Reliability, Network Congestion, Adaptive Routing, Predictive Modeling, Anomaly Detection, Network Performance, IoT Protocols, Real-Time Data Processing.