The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature's dependence on atmospheric parameters at an operational photovoltaic park in the semi-arid north of Chile using Artificial Neural Networks (ANNs). We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. The ANN model's effectiveness was evaluated by comparing it to five different deterministic models: the Standard model, King's model, Faiman's model, Mattei's model, and Skoplaki's model. Additionally, we examined the sensitivity of panel temperature to changes in air temperature, TSR, and wind speed. Our findings show that the ANN model had the best prediction accuracy for panel temperature, with a Root Mean Squared Error (RMSE) of 1.59 degrees C, followed by Mattei's model with a higher RMSE of 3.30 degrees C. We also determined that air temperature has the most significant impact on panel temperature, followed by TSR and wind speed. These results demonstrate that the ANN is a powerful tool for predicting panel temperature with high accuracy.