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Response Surface methodology is a statistical and mathematical approach that can be used to design, improve, and optimize a process. It is beneficial for analyzing the situation when multiple independent variables impact the dependent variable or response. It helps to reduce the noise of an experiment which in turn and guarantees optimization. It consists of a response variable which displays observed outcomes of experiments which is also referred to as output. It plays an important role with its involvement in new product design and development and also in upgrading the existing designs. It is an essential and very robust technique for data manipulation and analysis of research data in order to acquire a quality result or improve it. · One of the main advantage of RSM is large volume of data can be obtained by performing limited number of experiments. RSM in Improve phase 1.) All project or experiment decisions are clearly explained and outlined during this phase. Some of the decisions made under this topic include the research purpose, technique, and variables that may influence the outcomes. This procedure handles all the information required for the experimental strategy. 2.) Once the experiments is defined clearly and the data is collected, we get an idea of how many experiments are to be performed and how to perform them. 3.) After performing the experiment, the result is then compared and analyzed. The analysis of the outcome is compared to specific conclusions and defined research parameters/ the target or the expected outcome. 4.) A test can be performed to see whether the actual performance of the product in service matches the improvement identified in the results. The test here allows in determining the research gap. After the gap is identified, various other steps are considered to improve the experiments and make it achieve the target determined. RSM utilizes a number of surface visualization techniques. It visually assesses how various circumstances influence the answer. When a regression model is constructed as an outcome of interactions that involve multiple factors that predict, visualization helps to express the experimental results or answers more effectively. Some examples of graphical visualization tools known as response surface plots include effects, contour, residual or surface plots