The optimization process is applied to the machining operations in order to provide continual improvement in accuracy and quality of produced parts. The effects of machining parameters in milling operations, such as spindle speed, depth of cut and feed rate are investigated in order to minimize the surface roughness as well as the time of machining process. The effective machining parameters, such as depth of cut, feed rate and spindle speed in turning operations are investigated to minimize the surface roughness as well as the time of machining process. Also, machining parameters, such as peak current, gap voltage, duty cycle and pulse on time in Electro Discharge Machining (EDM) operations can be optimized in order to obtain the optimized material removal rate, tool wear, and surface roughness in part production process. To improve material removal rate, surface roughness, and spark gap in part production process using the wire EDM operations, machining parameters, such as spark on time, spark off time, input current are studied and optimized. To calculate optimized machining parameters, different optimization methods, such as Taguchi method, fuzzy logic algorithm, artificial intelligence, genetic algorithm, artificial neural networks, artificial bee colony algorithm, ant colony optimization and harmony search algorithm are used. As a result, time and cost of accurate production can be reduced to increase productivity in part production process using machining operations. In this paper, a review of machining parameter optimization is presented and future research works are also suggested. The main aim of the study is to review the challenges and limitations of the optimization techniques used in optimizing machining parameters. It has been observed that the research field can be moved forward by reviewing and analyzing recent achievements in the published papers.