This project investigates the nature of dark energy by analyzing Type Ia supernovae data from the Pantheon+ dataset, which includes over 1,000 supernovae observations. Using computational tools such as CAMB and Cobaya, the research employs Markov Chain Monte Carlo (MCMC) simulations to constrain key cosmological parameters, including the Hubble constant (H0), the matter density parameter (Ωm), and the dark energy equation of state parameters (w0, wa). By comparing observational data with theoretical models like ΛCDM, wCDM, and w0waCDM, this study aims to refine our understanding of the universe’s expansion and address discrepancies like the Hubble tension, ultimately contributing to a deeper knowledge of dark energy and the cosmos.