The objective of this study is to reveal the influence of the lead angle variation on tool wear in the process of face milling of compacted graphite iron with ceramic cutting tools. To achieve this goal, 36 milling experiments were carried out with different lead angles, cutting speeds and feed rates at the 2.5 mm constant depth of cut. The tool flank wear was strongly affected by the lead angle variations. SEM analyses of the cutting inserts were performed and experimental results have been modelled with artificial neural networks (ANN) and regression analysis. A comparison of ANN model with regression model is also carried out. The R-2 values for testing data were calculated as 0.992 for ANN and 0.998 for regression respectively. This study is considered to be helpful in predicting the wear mechanism of the ceramic cutting tool in the machining of compacted graphite iron. A quicker method for the estimation of tool life is proposed, which requires less consumption of workpiece material and tools.