PV power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, previously obtained data on the estimation of the power produced by a PV, which is cooled by L-shaped aluminum fins attached to the backside of the PV at different spacings, is used to predict the power produced by the PV. This is achieved by employing both neural network models and multiple linear regression (MLR) techniques to assess the correlation between power generated by PV with L-shaped aluminum fins and its input variables. Two distinct approaches were employed for this purpose. The first approach involved the conventional MLR model, while the second utilized a neural network, specifically the Multilayer Perceptron (MLP) model. The estimated outcomes were subsequently compared against the previously measured data. The MLR technique showed a great ability to identify the relationship between input and output variables, it was noted. The statistical error study provided evidence of data mining's acceptable accuracy when using the MLP model. Conversely, the results indicated that the Multilayer Perceptron Network (MLP) model exhibited the least ability to estimate the power generated by PV with L-shaped aluminum Fins.