Blasting is known as the most common approach for fragmenting rock in open-pit mines. Nevertheless, its side effects are not insignificant, for example, fly rock, ground vibration, dust, toxic by-products, air over-pressure, and back-break. These effects considerably alter the circumambient environment, particularly when pressure is higher than usual. This study proposed and compared four artificial intelligence models for predicting blast-induced air over-pressure, namely multi-layer perceptron (MLP), Random Forest (RF), isotonic regression (IR), and M5-Rules. The air over-pressure was selected as the output variable based on the input variables, i.e., stemming length (T), explosive charge per delay (W), burden (B), monitoring distance (R), and spacing (S). Several statistical performance indices, including coefficient of determination (R
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