Before a photovoltaic (PV) system is installed, a prerequisite modelling and performance analysis are carried out which estimates the performance parameters and reliability in operation of PV system. This paper proposes a neural network approach to performance prediction of PV Modules. Here the feed forward neural networks are used to predict I-V curve parameters as a function on input irradiance and temperature. K Fold cross-validation is used to validate model accuracy for determination of I-V curve parameters for five different technology modules, i.e., CdTe, CIGS, MICROMORPH, MUTICRYSTALLINE, MAXEON. A comparison is also drawn between the neural network predictor and the existing modeling procedures available. Model parameters have been determined following iterative, analytical and regression of known data points for seven and five parameter model of PV Modules. Among the electrical equivalent models, the seven parameter model is the most efficient model for performance prediction however commutation of model parameters in complex and tedious. Neural network model simplifies the computational process at the expense of higher error variance in comparison to electrical equivalent models. Further, a cascade implementation of the above two is designed and tested on Multicrystalline and Maxeon technology modules for higher model accuracy. The results obtained, verify the proposed cascaded model to be the most efficient model in comparison to independent models where mean bias error deviations are less than +/- 1% and error variance is reduced significantly. Also, a MATLAB based graphical user interface (GUI) is developed that can be used to predict the performance based on the analysis carried out. The proposed model is tested against a set of operating conditions and compared to the actual experimental values obtained using outdoor tests.