We present <em>SWEpy</em>, a new Python GPU-accelerated open-source finite volume (FV) software designed to solve the Saint-Venant system of shallow water equations (SWE) on unstructured triangular grids. <em>SWEpy</em> is designed for flexibility and performance, considering a well-balanced, positivity-preserving, and higher-order central-upwind FV scheme, intended to solve tsunami wave propagation, flooding, and dam-break scenarios, among others.</p> <p>In this regard, we enhance the minimization of numerical diffusion, a phenomenon frequently found in this sort of FV schemes, by using a second-order WENO reconstruction operator as well as a third-order strong stability-preserving Runge-Kutta time integrator. With this in mind, a modular software architecture is presented that can support a range of initial and boundary conditions and source terms.</p> <p><em>SWEpy</em>'s performance, stability, and accuracy are verified using canonical benchmarks, including Synolakis' conical island and Bryson's flow over a Gaussian bump, and further demonstrated in large-scale simulations of the 1959 Malpasset Dam failure and the Mw8.8 2010 Maule tsunami. <em>SWEpy </em>delivers high-resolution results on consumer-grade hardware, offering a user-friendly platform for both research and operational forecasting.