The next generation of wireless communication networks is expected to utilise unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance spectrum and energy efficiency. This work establishes a theoretical foundation for RIS-assisted UAV implementation, capitalising on the passive beamforming capabilities of RIS alongside the adaptable deployment and dynamic mobility of UAVs to enhance internet-of-things (IoT) networks performance. A comprehensive framework for RIS-assisted UAV IoT data collection is represented and optimised to enhance critical performance metrics, including the quantity of served IoT devices and achievable data rates. The optimisation strategy deploys a deep reinforcement learning (DRL) algorithm to fine-tune UAV trajectories and IoT device scheduling decisions, complemented by a codebook for RIS beamforming to optimise the RIS phase shift matrix. This integrated approach addresses the ever-increasing demand for efficient data collection in wireless IoT networks. Simulation results show substantial improvements in system performance, demonstrating the efficiency of the proposed algorithm. By coordinating the RIS phase shift matrix and UAV trajectory planning, the proposed framework achieves improvements in terms of the number of served IoT devices and achievable data rates. For example, compared to baseline methods, our approach outperforms benchmark scenarios by over 50% in terms of the number of served devices. The results reveal the potential of RIS-assisted UAV solutions in meeting the increasing demands of wireless IoT networks.