The Internet of Things (IoT) plays a significant role in smart agriculture, which is now on the agenda of intelligent farming systems, so better communication networks are required. Employing dynamic routing in these networks lets the data be transmitted with the right timing and reliability. This paper proposes a new paradigm that employs Fuzzy Q-Learning to improve the routing aspect of IoT networks for smart farming. The proposition uses fuzzy logic to deal with fuzzy information and uncertainties associated with agro-scenarios, like pest detection, irrigation scheduling, environmental monitoring, etc. Combining Q-Learning with Fuzzy logic would allow the system to make specific predictions of the state of the network to make the right proactive routing decisions, leading to increased overall network performance by reducing latency, packet loss ratio, and energy consumption efficiency. The performances are simulated using the introduced method in an imaginative farming scenario and compared against conventional routing algorithms. The simulation results show that the proposed solution could achieve superior network efficiency and reliability performance compared to other current solutions.