Analytical and empirical models analyze complex and non-linear interactions between the input–output parameters of the system. This is very important in the case of photovoltaic systems to understand their real performance potential. On the other hand, manufacturers of photovoltaic panels rate the maximum performance of the system under fixed lab conditions as per standard testing conditions (STCs) or nominal operating cell temperature (NOCT) standards of IEC. These ratings do not provide the actual production potential of the system in a field with fluctuating conditions of irradiance and temperature. For the case of a concentrated photovoltaic (CPV) system, utilizing multi-junction solar cells (MJCs), there is no commercial tool available to analyze the performance and production, despite some recent empirical models that also require post-processing of experimental data to be used in conventional models. In this study, an artificial neural network (ANN)-based performance model is presented for a multi-junction solar cell, which is not only convenient to apply but can also be easily expanded to predict the real-field performance of the CPV system of any designed size. In addition, the ANN-based model showed a high accuracy of 99.9% in predicting the performance output of MJCs as compared to diode-based empirical models available in the literature. The irradiance concentration at the cell area and the cell temperature are taken as inputs for the neural network. If both of these parameters are known, then the cell efficiency as an output can accurately predict the CPV performance for a field operation.