Higher temperatures during turning adversely affect the cutting tool owing to thermal softening. The heightened material dispersion compromises the quality of the machined component. This research encompasses a study that experimentally examines and statistically evaluates the impact of various cutting settings on the turning performance of AISI 1045 steel. A statistical method such as analysis of variance (ANOVA) and full factorial design were used to accomplish this study. Furthermore, this paper presents the results of a series of experiments that used a hybrid artificial neural network (ANN) and genetic algorithms (GA) to optimize the cutting temperature to improve the surface quality. The results revealed that the machined surface undergoes significant tool type, cutting speed, feed rate, and depth of cut. A typical carbide insert tool, cutting speed of 80 m/min, a depth of cut of 0.5 mm, and a feed rate of 0.045 mm/rev were employed in the experiments to achieve a minimum cutting temperature of 412.9 °C. Utilizing a hybrid ANN-GA with the same values of parameters yields a cutting temperature of 436.7 °C.  Consequently, ANN-GA has improved the cutting temperature and is more effective in attaining the desired result. Therefore, the cutting temperature of hybrid algorithms has been enhanced, rendering them more efficient.