Machining of hardened tool steels concerns severe unstable surface integrity, tool wear, and complex thermo-mechanical interfaces, presenting challenges for sustainable manufacturing. This work aims to investigate the effect of nano-aluminium oxide (Al2O3) minimum quantity lubrication (MQL) on the machinability of heat-treated high-speed tool steel (YXR-7) and to evaluate the ability of data-driven models in predicting machining responses under limited experimental conditions. A total of 81 controlled milling experiments were performed to estimate surface roughness (Ra), material removal rate (MRR), and tool wear rate (TWR) under dry and nano-MQL conditions. Experimental findings confirmed that nano-MQL decreased surface roughness by 65.61% and tool wear rate by nearly 56% compared to dry machining, while maintaining higher productivity. FESEM (Field emission scanning electron microscopy) observations showed an evolution from severe adhesive–diffusive wear in dry cutting to moderately mild abrasive–oxidative wear with tribofilm formation under nano-lubrication. XRD (X-ray diffractometry) analysis confirmed the presence of oxide and various carbide phases, supporting the experience of stress-assisted tribo-chemical relations. To investigate predictive performance, Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Feedforward Neural Network (FNN), and Gaussian Process Regression (GPR) models were utilised by cross-validation. For the available dataset, simpler regression models exhibited stable generalisation behaviour, while more complex nonlinear models established sensitivity to data scale. The integrated experimental and modelling approach provides insight into lubrication-driven wear mechanisms and offers practical guidance for improving process efficiency and sustainability in hard milling applications.