<p>The impact of random machining conditions on the final quality of the work piece and tool is highly effective on industrial performance. The current study examines the impact of cutting parameters on the surface roughness of the work piece and tool wear during the turning of Ti-6Al-4V titanium alloy on an all-gear lathe. A full factorial experimental design was used to conduct the trials by varying parameters such as tool type, method of cooling, cutting speed, and feed rate. Using HSS and solid carbide tools, the experiments were carried out under various cooling conditions (dry, minimal quantity lubrication (MQL), and nano-cutting fluid) and under various turning parameters (feed rate and cutting speed). The Mitutoyo SJ-201 surface tester was used to measure the surface roughness, and tool wear was measured through an optical microscope. The experiment’s findings demonstrate that both cooling method and tool type have a significant influence on tool wear and sample surface roughness. The experimental outcomes have been analyzed through the TOPSIS method with equal weight values, and the carbide tool with nano-MQL supply at 225&#xa0;m/min cutting speed and 0.09&#xa0;mm/rev feed rate is the optimal parameter for the combined objectives. The AdaBoost algorithm exhibited excellent performance in predicting the outcomes with superior accuracy. The decision tree structure has highlighted that both experimental outcomes are influenced by the cooling method. SEM analysis of the tool and workpiece surface revealed the presence of grooves, scratches, adhesion layers, and debris through dry machining conditions, while the nano-MQL machining condition represented lesser tool wear and higher surface finish.</p>