<p>The inertial strategy has been widely utilized to accelerate proximal gradient methods for nonconvex nonsmooth optimization problems. Recently, the gradient extrapolation technique has also been adopted to further enhance the acceleration of these methods. Inspired by the effectiveness of both techniques, in this paper, we propose a general inertial proximal gradient method with gradient extrapolation, named GiPMGE. Compared to existing methods, our proposed GiPMGE not only covers some classic methods, but also offers more general and flexible choices for the inertial, gradient extrapolation, and stepsize parameters. Under the assumption that the merit function satisfies the Kurdyka-Łojasiewicz property, we prove that the sequence generated by GiPMGE globally converges to a critical point and derive the corresponding convergence rates. Additionally, we conduct some numerical experiments to demonstrate the advantage of GiPMGE.</p>