<p>Titanium alloys, renowned for their exceptional strength, corrosion resistance, and lightweight properties, play a crucial role in numerous mechanical engineering applications. This study addresses the machining complexities of titanium alloy Ti6Al4V by focusing on dry turning operations under varying hardness conditions. Two distinct hardness levels 32 HRC and 38 HRC are investigated using uncoated carbide tools. Experimental parameters including cutting speed, feed rate, and depth of cut are systematically varied to assess their effects on machining performance metrics such as surface roughness, cutting forces, power consumption, and tool wear. Through comprehensive experimentation and mathematical modeling, this research aims to explain the influence of material hardness on machining behavior and optimize process parameters for enhanced efficiency and quality. Ultimately, a multi-objective optimization was conducted and discussed regarding multi-objective artificial Vultures optimization algorithm “MOAVOA” and multi-objective optimization genetic algorithm “GA” methods. The MOAVOA algorithm has demonstrated highly satisfactory results in addressing multi-objective optimization problems and has outperformed the genetic algorithm. The results from the MOAVOA algorithm optimization indicate that the optimal cutting conditions, which achieve a balance between surface roughness (Ra), cutting force (Fz), and cutting power (Pc), fall within the following ranges: cutting speed (Vc) of 90.5–115.32 m/min, feed rate (<i>f</i>) of 0.08–0.81 mm/rev, and depth of cut (ap) of 0.103–0.166 mm for the treated workpieces examined. The findings offer valuable insights into the machinability of Ti6Al4V and provide practical recommendations for improving machining processes in mechanical engineering applications.</p>