In this paper, we introduce an inertial accelerated iterative algorithm for approximating a common solution of a minimization problem and a fixed point problem for quasi-pseudocontractive mapping in a real Hilbert space. Using the algorithm, we prove a strong convergence theorem for approximating a common solution of a minimization problem and a fixed point problem for quasi-pseudocontractive mapping. Furthermore, we give an application of our main result to solve convexly constrained linear inverse problems, and we also present a numerical example of our algorithm to illustrate its applicability.