The development of autonomous underwater docking systems is essential due to the limited endurance of autonomous underwater vehicles(AUVs), which necessitates their frequent retrieval by surface vessels for recharging- a process that can be time-consuming and expensive. These docking systems, when coupled with marine energy devices like wave energy converters (WECs) or when configured to harness power from cabled oceanographic observatories, can extend the longevity of AUV missions and reduce the dependence on ship support through their autonomous capabilities. Consequently, this integration leads to a significant reduction in carbon footprint and operational costs. Not only does it benefit the environment by replacing fossil fuel-dependent vessels with renewable energy sources, but it also provides a promising solution for expanding our understanding of the ocean by enabling AUVs to engage in long-term deployments, such as bathymetric mapping, inspection of submerged structures, and monitoring ocean conditions in deep waters. However, achieving autonomous docking in challenging conditions, including strong ocean currents and wave forces, remains an active area of research. Therefore, we present a docking framework that incorporates flow state estimation into the design of a model predictive controller (MPC) for achieving autonomous underwater docking with a WEC in diverse ocean conditions. Furthermore, this framework adequately addresses the influence of wave forces on the AUV.