Abstract This study explores advanced methods to improve the accuracy of hydrocarbon property predictions in oil reservoir fluids, utilizing novel data from an Iranian oil reservoir. The objectives include calculating molecular weights, specific gravity, and molar ratios of hydrocarbons (C11–C50) while evaluating the performance of models for critical properties and phase equilibria. Key approaches involve the gamma distribution and single carbon number (SCN) techniques for hydrocarbon characterization, alongside flash calculations using cubic and non-cubic equations of state, including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Perturbed Chain-SAFT (PC-SAFT). Empirical data were generated using the Pederson regression method and compared with predictions from the gamma distribution function. Results indicate that the gamma distribution method reduces prediction errors by 50 %, with deviations of 5.19 % compared to 10.39 % for the SCN method. Among the equations of state, PC-SAFT achieved the highest accuracy, predicting vapor phase mole fractions with a deviation of 0.0172 % and gas and liquid densities with deviations of 22.125 % and 0.24 %, respectively. The novelty of this study lies in integrating unique field data from an Iranian reservoir with advanced modeling techniques, providing a reliable framework for reservoir fluid analysis. These findings contribute to optimizing oil recovery and improving hydrocarbon prediction accuracy.