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Gas Turbine (GT) power plants suffer from sensitivity to ambient-air temperatures, high fuel consumption, and a high amount of waste heat dumped into the ambient. Various solutions were proposed to solve these drawbacks, which could simultaneously solve at most two problems, usually at the expense of the third. For instance, inlet-air cooling can reduce ambient-air temperatures but will result in increased fuel consumption. However, in this work, a novel cascaded system was integrated into a combined cycle, capable of simultaneously solving all the aforementioned GT drawbacks. Parabolic trough collectors were used to preheat the air at the combustion chamber inlet initially. The collectors were then used to drive an absorption inlet-air cooling cycle to control the ambient air temperature at the compressor’s inlet. The proposed integration enhanced the overall performance of the system and obtained a 6.87 % relative increase in power output at the design point, a 19.45 % relative boost in thermal efficiency, and a 10.53 % relative decrease in fuel consumption. A parametric study to assess the operating parameters' effect on the overall system’s performance and the environment was also conducted. Additionally, a unique linear regression based on the parametric study was applied; to enable multi-objective optimization using a Generic Algorithm with efficiency and capital cost as conflicting objectives. The system’s optimal operation was found to exhibit a thermal efficiency of 61.1 %, with a payback period of 2.5 years. Also, this study uses the 2nd law of thermodynamics to estimate the maximum available energy, calculate the electric exergy efficiency, and explore the maximum irreversible exergy destruction in the system’s components. Artificial Neural Network was employed to develop a multi-objective optimization. Spider diagrams investigated the effect of varying several key operating parameters on the performance of the system, identifying the Gas Turbine as the highest irreversibility sub-unit and the solar field parabolic trough collectors as the second. Artificial neural networks with multi-objective optimization maximized the electric exergy destruction by 34.54 % and minimized the exergy destruction by 13.68 %, relative to the corresponding values from the simple design point.