This study explores the influence of dust on optical properties such as transmittance, absorptance, and emissivity of photovoltaic (PV) modules using over 300 experimental readings from various dust types. These readings were collected during regional storms and ground sources, data encompass different weight levels. Incorporating 690 global datasets and leveraging Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) in MATLAB, the study integrates key dust chemical components (Si, Fe, Ca, Al) and weight to predict the PV optical properties. This approach enhances models’ predictive accuracy across diverse environmental settings, which in turn enables more accurate forecasting of PV power output and thermal behavior under varying dust conditions, as these optical properties govern the module equations. Additionally, comparative analysis with existing literature shows superior accuracy, achieving Mean Squared Errors (MSEs) of 1.8 and 8.44, surpassing previous benchmarks. Results underscore the global efficacy of our methodologies in revealing dust’s impact on PV module thermal behaviour and efficiency.