This research examines a solar driven Kalina power plant, which utilizes a Parabolic Trough Collector (PTC) to exploit the solar energy. Energy, exergy, and the exergoeconomic model were developed, and an investigation was carried out to assess the impact of the wind speed and the working pressure of the heat exchangers on the system performance. Also, a transient hourly evaluation with reference to real weather data was carried out for the Tehran location. Furthermore, a machine learning (ML) approach was applied to estimate the performance by means of local wind speed, solar irradiation, and incident angle of solar radiation. The findings of the study revealed that the PTC panels have the highest exergy destruction ratio of 80.77% by the rate of 348.216 kW. In spite of this rate, the Kalina turbine production was 25.079 kW. With an increase in the wind speed, the destruction rate of the solar collector unit rose by 1.16%, and the overall exergy efficiency reduced from 5.05% to 4.891%. Also, the specific cost of the produced power increased to 76.56 $/GJ by 2.5%. Furthermore, the hourly analysis revealed that the specific cost decreased to a minimum value of 72.94 $/GJ at 12:00 for Tehran local climate conditions. Also, the results of the machine learning exhibit a prediction model with R2 and RMSE metrics of 0.89997 and 0.0025562, respectively.
