Partial Differential Equations (PDEs) are widely used across physics, engineering, finance, biology, and other fields to model various phenomena. PDEs can be classified into elliptic, parabolic, and hyperbolic, depending on their properties such as diffusion, or wave-like behavior. Hyperbolic equations are suitable for simulating dynamic phenomena with wave-like behavior such as flow propagation. Examples include Euler equations, shallow water equations, and classical wave equation. Due to the complexity of the 2d hyperbolic equations, numerical methods like finite difference and finite volume are often used for their solution. Among these, the finite volume method such as the cell-centered approach, derived from the conservation laws, is fundamental for solving hyperbolic equations, offering flexibility in dealing with complex geometries. Meshless methods like the radial basis function (RBF) method have recently gained attention for their simplicity and low computational cost, dividing the domain into particles and representing equations using RBFs. However, meshless methods face solvability issues and heavily rely on the shape parameter and distribution of collocation points. This article aims to quantitatively compare the costs, accuracy, and efficiency of RBF-MQ and FVM-CC methods for solving 2d wave equations. The study includes analyzing the behavior and performance of meshless and mesh-based methods, estimating accuracy using norm L2 and L∞, sensitivity to the shape parameter and the distribution of data in complex geometry, and calculating CPU runtime for each method.