This study aimed to assess and contrast the predictive power of two machine learning methods, novel linear regression and centroid, in estimating the efficiency of an image lacquering procedure. The goal was to determine which algorithm forecasts the lacquering procedure's effectiveness more correctly. When it came to the study, the sample size was ten, and the G power was zero. This was the case regardless of the method that was utilized. Seven was the quantity that was considered to be necessary in order to have adequate statistical power. Eight was the number that was taken into consideration. In the course of carrying out a meta-analysis, the results showed that the p-value was substantially lower than zero. The statistical investigation indicated that the two algorithms were significantly different from one another, that they provided an outstanding level of statistical significance, and that the changes that were seen were definite and not a function of variance. In addition, the study demonstrated that the two algorithms provided an exceptional degree of predictive power. It was necessary to do this task in order to achieve the desired result. In order to produce a forecast about the efficiency of the lacquering process, it was possible to make use of the specific weights and configurations that are utilized by the centroid approach. In the more nuanced element of accurately estimating the effectiveness of the lacquering process from photographs, it was determined that the novel linear regression strategy is significantly superior to the centroid approach. This was established through the process of data analysis.