This study focused on the derivation and modelling of dynamic equations for synchronous generator for hydro power plant. Hydro power plant mainly consists of three sections, governor (controller), hydro servo system and hydro turbine. The hydro turbine governor is usually coupled to a synchronous generator to drive the shaft so that the mechanical energy of turbine is converted to electrical energy. Accurate modeling of hydraulic turbine and its governor system is essential to depict and analyze the dynamic system response. In this work, both hydraulic turbine and turbine governor system were modeled. The hydro turbine model is designed using penstock and turbine characteristic equations. The simulation model is developed using MATLAB/SIMULINK. The dynamic response of the governing system to the disturbances such as load variation on the generator parameter during fault was presented. The results graphically demonstrate the effect of load variation on generator parameters under a three phase to ground fault. The transient behavior of generator voltage, current and the rotor speed are also captured. Similarly, two hydropower dams along the River Niger ( Kainji and Jebba dams) in Nigeria were analyzed for energy generation using multilayer perceptron artificial neural network. Total monthly historical data of Kainji and Jebba hydropower reservoirs’ variables and energy generated were collected for a period of forty-two years (1980-2021) and (1994-2021) for the network training. These data were divided into training, testing and holdout data set. The neural network analysis yielded a good forecast for Kainji and Jebba hydropower reservoirs with correlation coefficients of 0.89 and 0.77 respectively. These values of the correlation coefficient showed that the networks are reliable for modeling energy generation as a function of reservoir variables for future energy prediction.