Hastelloy C276 is extensively used in aerospace, chemical, and high-temperature engineering systems, yet its poor machinability leads to rapid tool degradation and reduced productivity. This study develops a comprehensive machine-learning (ML) framework to model and predict flank-wear progression during the turning of Hastelloy C276 under Dry, Minimum Quantity Lubrication (MQL), and nanoparticle-assisted MQL environments. A series of controlled machining experiments were performed by varying cutting speed, feed, depth of cut, and machining length, generating more than 700 labeled wear samples measured using optical microscopy. Four ML models—Ridge Regression, Decision Tree, Random Forest, and Support Vector Regression— were trained using five-fold cross-validation for hyperparameter optimization, and their final performance was evaluated on an independent test dataset. Among them, Random Forest exhibited the highest predictive accuracy (R2 = 0.982, MAE = 0.004 mm, RMSE = 0.006 mm), effectively capturing nonlinear wear behavior associated with thermal-mechanical interactions. Experimental results confirmed the strong influence of lubrication environment on tool life, with nano-MQL reducing average flank wear by 28–35% compared to Dry machining due to enhanced cooling and tribo-film formation by hBN nanoparticles. Feature-importance analysis further identified lubrication condition, machining length, and feed rate as the dominant predictors governing wear evolution. The study demonstrates that reliable tool-wear prediction can be achieved using machining parameters alone—without additional sensors—highlighting the potential of ML-driven frameworks for future intelligent tool-condition monitoring and sustainable machining of difficult-to-cut superalloys.