Employee development is an important component of human resource management that facilitates human development within the organization. The purpose of the this study is to develop a machine learning algorithm to predict employee growth. For a realistic increase prediction, a modified Additive Regression Classifier is mainly used. Modified Additive Regression Classifier is primarily used for automatic campaign forecasting, and its performance is compared to other devices study design such as Additive Regression Classifier, XGBoost (XGB), Support Vector Machines (SVM), Dispatch Systems regression (LR), random forest (RF), and artificial neural networks (ANN). Monitoring the effectiveness of these Machine learning algorithms for predicting professional development are evaluated through a comprehensive evaluation process which applies analytical metrics to employee analytics data management. The results show that compared to the normal machine learning methods, artificial neural network (ANN) and additive regression classifier models are employed alright. In addition, a modified additive regression classification strategy with an accuracy of 95.30% is proposed. It outperforms all other measures considered.