<p>The present study investigates the parametric influence of machining and nanofluid-assisted minimum quantity lubrication (NF-MQL) variables on surface roughness and cutting force during the milling of AISI 5160 steel. A structured experimental design based on the Taguchi approach was employed to evaluate the effects of cutting speed, feed rate, depth of cut, MQL air pressure, nanofluid flow rate, and nanoparticle concentration. Surface roughness and cutting force were selected as key performance indicators to assess surface integrity and machining load, respectively. Statistical analyses were carried out using signal-to-noise ratio analysis, analysis of variance (ANOVA), and regression modelling to identify significant factors and establish predictive relationships. The results reveal that surface roughness is predominantly influenced by feed rate and cutting speed, whereas cutting force is strongly governed by depth of cut, followed by cutting speed and nanofluid flow rate. NF-MQL parameters, particularly fluid flow rate, play a vital role in enhancing tribological conditions, leading to reduced friction, lower cutting forces, and improved surface finish. The developed regression models exhibit high predictive accuracy, with coefficients of determination exceeding 96% for surface roughness and 99% for cutting force. Residual diagnostics confirm the models’ validity and robustness. Optimal machining conditions were identified that simultaneously minimise surface roughness and cutting force, whereas poor performance was associated with low cutting speeds, high feed rates, large depths of cut, and insufficient lubrication. Overall, the study shows that NF-MQL is a viable lubrication strategy for improving machining performance in the milling of high-strength AISI 5160 steel. Furthermore, the research will contribute to the development of optimised NF-MQL parameters and predictive machining models for a material that has received little attention in the existing literature.</p> Graphical abstract <p></p>