Performance evaluations are conducted with the intention of establishing each worker’s level of commitment to the company. Numerous businesses are challenged by the issue of employee attrition, which occurs when talented workers with years of expertise leave the firm on a regular basis. On the other hand, employee turnover can be caused by a wide variety of factors, and it can be challenging for the HR manager or the head of each department to recognize warning indications in a timely manner. Forecasting the performance of employees is a vital part of a successful business. As a result, we provide a methodology for anticipating employee attrition in this study so that we can carry out talent management strategies that were previously done ex post. A model of predication for employee attrition was built for this study using 30 factors. We use 1470 entries from the “IBM HR Analytics Employee Attrition and Performance data” for this purpose. We utilize the two-stage staking ensemble model, which integrates the basic models of Random Forest, K-nearest Neighbor, Naive Bayes, and Decision Tree with the meta-model of Logistic Regression for forecasting. Our suggested two-stage stacking model has an accuracy of 88.01%. Additionally, we compared our suggested model to Decision Trees, Support Vector Machines, and Gaussian Naive Bayes, three major machine learning model.