The study investigates the impact of DESI 2024 data on dark energy models, analyzing three categories: thermodynamic-based models, Taylor-expanded barotropic factors, and parametric dark energy parameterizations. Using Monte Carlo Markov chain analyses, the researchers find that the best-fit model is not the Chevallier-Polarski-Linder (CPL) parametrization but a more complex log-corrected dark energy term. They also find that the product $ r_{d}h_{0} $ is anticorrelated with mass, and excluding a problematic data point at $ z = 0.51 $ aligns results with the $ \Lambda $ CDM model. The study concludes that future data will be essential to determine if dynamical dark energy is in tension with the $ \Lambda $ CDM model. The results suggest that the $ \Lambda $ CDM model remains statistically favored, and while some dark energy models show promise, they are not significantly better than the standard model. The study highlights the importance of considering different model parameters and the potential for future data to clarify the nature of dark energy.The study investigates the impact of DESI 2024 data on dark energy models, analyzing three categories: thermodynamic-based models, Taylor-expanded barotropic factors, and parametric dark energy parameterizations. Using Monte Carlo Markov chain analyses, the researchers find that the best-fit model is not the Chevallier-Polarski-Linder (CPL) parametrization but a more complex log-corrected dark energy term. They also find that the product $ r_{d}h_{0} $ is anticorrelated with mass, and excluding a problematic data point at $ z = 0.51 $ aligns results with the $ \Lambda $ CDM model. The study concludes that future data will be essential to determine if dynamical dark energy is in tension with the $ \Lambda $ CDM model. The results suggest that the $ \Lambda $ CDM model remains statistically favored, and while some dark energy models show promise, they are not significantly better than the standard model. The study highlights the importance of considering different model parameters and the potential for future data to clarify the nature of dark energy.