To enhance the financial viability and optimize the design of solar energy projects, accurately assessing the performance and output of PV modules at the installation site is crucial. While various empirical and theoretical predictive models exist in the literature, their limitations and complexities often render machine-learning models a preferable alternative. This study evaluated the accuracy of three different machine learning models, Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Extra Trees Regression (ETR), for predicting PV output, comparing their performance against a year’s worth of ground measurements from a CdTe PV module installed in a semi-arid climate location. The results indicate that all three models effectively simulated CdTe PV production based on meteorological parameters as inputs, achieving R-squared values exceeding 0.99 when comparing predicted outputs to actual measurements. Further analysis of the models’ performance has demonstrated that the ETR model outperformed the ANN, followed by the MLR, which tended to slightly underestimate the actual outputs.