This work aims to improve channel prediction accuracy, and a smarter and environmentally sensitive approach is developed by integrating IoT technologies. IoT-based sensors can monitor environmental parameters (e.g., temperature, humidity, mobility, presence of obstacles, etc.) that affect the channel estimation process in real-time, and these data can be dynamically integrated into channel coefficient extraction algorithms. In addition, edge units in the IoT infrastructure can perform channel estimation operations with low latency at a point close to the data source. This significantly improves performance, especially in time-sensitive and data-intensive applications (such as intelligent transport systems, industrial automation or health monitoring systems). An IoT-enriched channel prediction model reduces the reliance on traditional pilot signals, enabling a more flexible, agile and situation-aware communications infrastructure.