<p>The frequency deviations and harmonic generation of the two-area power system network under the load disturbances are major challenges of the research. Such variations in frequency also affect other parameters as well. The conventional way of the load frequency control of two-area system is too complex and need more variable for computation In order to overcome such issues, A multi-objective function has been developed by considering the effect of all disturbances. Further, the multi-objective function will be trained by a proposed intelligent controller to resolve such problems. The proposed intelligent controller has been called as heuristic grasshopper optimization neural network (h-GO-NN) controller, which is developed by combining the two topologies, namely the grasshopper optimization algorithm and artificial neural network. The proposed controller is simple, and need less computation variables, which is the novelty of the research. The first step in the entire analysis is the mathematical modelling of the different generating units in each area, including conventional sources (CS), wind turbines (WTS), and solar photovoltaic (PV), all of sources&#xa0;are connected to the common grid. The h-GO-NN controller is used to simulate two area networks in order to examine the different parameters. A number of performance parameters will be measured, including voltage deviation, frequency deviations, area control error (ACE), and total harmonic distortion (THD) of grid voltage. Variable load disturbance, variable solar irradiation, and variable wind operation are the three scenarios in which all such evaluations will be examined. At the end, it is observed that the least frequency deviation, least voltage deviation, minimum ACE and minimum THD (%) have been attained with the proposed h-GO-NN controller in contrast to existing techniques which is one of greatest achievement of proposed controller.</p>