Inconel 718 possesses exceptional properties that negatively affect tool life and other key indicators of the cutting mechanism. Owing to the unprecedented failure states of carbide inserts under the synergistic impact of processing conditions and the complex superalloy’s metallurgical properties, the imagery signals contain smeared noise, which affects the predictive efficiency of data analytics during Tool Condition Monitoring (TCM). Previous studies applied image processing techniques, such as edge segmentation, detection, auto-encoders, textural, fractal, Fourier, and wavelet analysis, to extract features from tool wear signals despite being inefficient under complex wear morphology. Therefore, this study applies a more efficient data-mining analytic called the multi-sectional singular value decomposition (multi-sectional SVD) for dimensionality reduction and extraction of features from the complex imagery signals, enhancing the predictive efficiency of Machine Learning (ML) during TCM. To achieve this, an interrupted climb-milling of Inconel 718 was conducted at various speeds, feeds, and axial depth of cut to generate the dataset through the in-process acquisition of tool wear images, as well as measurement of the progressive VB for the PVD-TiAlN/NbN coated carbide inserts. Then, the multi-sectional SVD-based ML was employed to process the imagery signals and extract latent features that combined with process parameters to predict VB. After validating the predicted against the actual VB values, the model yielded a Mean Average Percentage Error (MAPE) of 2.36%, indicating its effectiveness in predicting VB profile under complex flank wear morphology. Furthermore, the system was utilized to design an effective cutting condition, where the multi-stage adjustment of speed and feed was employed to reduce the VB rate in the early cutting stage.