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A new parametrization of the phenomenological Hubble parameter is proposed toexplore the issue of the cosmological landscape. The constraints on modelparameters are derived through the Markov Chain Monte Carlo (MCMC) method byemploying a comprehensive union of datasets such as 34 data points from cosmicchronometers (CC), 42 points from baryonic acoustic oscillations (BAO), arecently updated set of 1701 Pantheon$^+$ (P22) data points derived from TypeIa supernovae (SNeIa), and 162 data points from gamma-ray bursts (GRB). Thekinematic behavior of the models is also investigated by encompassing thetransition from deceleration to acceleration and the evolution of the jerkparameter. From the analysis of the parametric models, it is strongly indicatedthat the Universe is currently undergoing an accelerated phase. Furthermore,the models are compared by using the Akaike Information Criterion (AIC) andBayesian Information Criterion (BIC), so that a comparative assessment of modelperformance can be available.

Governing accelerating Universe via newly reconstructed Hubble parameter by employing empirical data simulations