Employee promotion is an important aspect in human-resource management process. Thus, it is crucial to correctly decide, whether an employee should or should not be promoted based on its current and past ratings. For that purpose, the research has been carried out to develop employee`s promotion prediction models by using different machine-learning classification algorithms. In this research, experiments on simulated dataset was performed using Gradient Boosting Classifier, Random Forest Classifier and Keras Neural Network. Through a complex assessment process, the performance of these supervised machine learning algorithms for predicting employee advancement was analyzed using assessment metrics. Our study uses simulated employee data as the training dataset, and we developed a web application for our study to display forecast results on new inputs.