This study presents an integrated approach combining business analytics and machine learning to predict employee performance. The proposed model uses data from various sources, including performance metrics, staff data, and contextual factors, to build accurate predictive models. The research examines aspects of data analytics such as feature engineering, data preprocessing, model selection, and evaluation metrics. The findings show that the integrated approach is efficient in forecasting workforce performance, offering valuable insights for informed decision-making in talent management and resource allocation. Employee performance prediction is crucial for organizational success, as high-performing staff contribute significantly to achieving company objectives and maintaining a competitive edge. However, traditional performance assessment methods often rely on subjective evaluations, which can be inconsistent or biased. To address these limitations, the study explores the integration of business analytics and machine learning techniques to enhance the accuracy and reliability of performance forecasting. The research aims to develop and evaluate integrated approaches that combine machine learning and business analytics for forecasting employee performance. It seeks to identify key variables and factors influencing performance, examine how business analytics can extract meaningful features for prediction, explore how machine learning algorithms can develop predictive models, and assess the practicality and effectiveness of the integrated approach. The study outlines five commonly used methods in predicting human resources: descriptive analytics, prescriptive analytics, predictive modeling, natural language processing, and cluster analysis. It also discusses four commonly used machine learning methods: linear regression, random forests, neural networks, and support vector machines. The integration of business analytics and machine learning provides companies with unprecedented insights into employee performance. The proposed model involves data collection, preprocessing, data profiling, feature selection, model selection, training and testing data, model optimization, and deployment and monitoring. The study concludes that the integration of business analytics and machine learning offers companies an opportunity to forecast employee performance more accurately, enhancing decision-making processes and overall company performance. Continuous monitoring and adjustment are essential to maintain the model's performance over time.This study presents an integrated approach combining business analytics and machine learning to predict employee performance. The proposed model uses data from various sources, including performance metrics, staff data, and contextual factors, to build accurate predictive models. The research examines aspects of data analytics such as feature engineering, data preprocessing, model selection, and evaluation metrics. The findings show that the integrated approach is efficient in forecasting workforce performance, offering valuable insights for informed decision-making in talent management and resource allocation. Employee performance prediction is crucial for organizational success, as high-performing staff contribute significantly to achieving company objectives and maintaining a competitive edge. However, traditional performance assessment methods often rely on subjective evaluations, which can be inconsistent or biased. To address these limitations, the study explores the integration of business analytics and machine learning techniques to enhance the accuracy and reliability of performance forecasting. The research aims to develop and evaluate integrated approaches that combine machine learning and business analytics for forecasting employee performance. It seeks to identify key variables and factors influencing performance, examine how business analytics can extract meaningful features for prediction, explore how machine learning algorithms can develop predictive models, and assess the practicality and effectiveness of the integrated approach. The study outlines five commonly used methods in predicting human resources: descriptive analytics, prescriptive analytics, predictive modeling, natural language processing, and cluster analysis. It also discusses four commonly used machine learning methods: linear regression, random forests, neural networks, and support vector machines. The integration of business analytics and machine learning provides companies with unprecedented insights into employee performance. The proposed model involves data collection, preprocessing, data profiling, feature selection, model selection, training and testing data, model optimization, and deployment and monitoring. The study concludes that the integration of business analytics and machine learning offers companies an opportunity to forecast employee performance more accurately, enhancing decision-making processes and overall company performance. Continuous monitoring and adjustment are essential to maintain the model's performance over time.