Machining of Inconel 718 causes rapid tool failure, which affects the tooling cost and dimension tolerance of the components. Literature attributed flank wear as the dominant failure criterion that determines tool life during the milling operation. Flank wear width (VB) can be measured using digital microscopes or predicted in-process by a machine vision-based tool condition monitoring (MV-TCM) system. In MV-TCM, geometric and textural features are extracted from the flank wear region to represent VB progression. However, the leading cutting edge, where flank wear is measured, experiences progressive chipping and built-up edge when machining Inconel 718. These failure modes distract pixel distribution and luminosity, creating complex flank wear features on the leading cutting edge. Nevertheless, the wear region extracted from the side cutting edge shows a consistent change in features that can be used to predict flank wear progression under such failure modes. In addition, the scale-invariant fractal dimension can complement the geometric parameters, improving the reliability of features used to predict flank wear. This paper presents a multi-layer perceptron neural network (MLPNN) that was trained using a synergy of geometric and fractal features extracted from the side cutting edge of square inserts to predict flank wear progression during face milling of Inconel 718. The MLPNN shows an accuracy of 95.5% and a mean absolute percentage error of 1.099% during MV-TCM. The paper shows the potential of applying an in-process MV-TCM expedited by spatial binary features to estimate flank wear progression when milling Inconel 718.