Variance analysis remains a foundational tool in management accounting, traditionally used to compare actual performance with budgeted expectations and identify deviations in costs, revenues, and operational outcomes. This review examines the relevance, strengths, and limitations of traditional variance analysis methods in contrast with emerging predictive analytics approaches. Traditional methods, including material, labor, overhead, sales, and profit variances, have long supported managerial control by highlighting performance gaps and enabling corrective action. Their appeal lies in their simplicity, structured format, and suitability for routine financial monitoring. However, these methods are often criticized for their retrospective nature, delayed feedback, limited adaptability to dynamic business environments, and weak capacity to explain complex interrelationships among operational variables. In response to these limitations, predictive analytics has gained attention as a more proactive and data-driven alternative. By applying statistical modeling, machine learning, data mining, and forecasting techniques, predictive analytics enhances variance analysis through real-time insights, pattern recognition, anomaly detection, and forward-looking decision support. This review compares both approaches across dimensions such as timeliness, accuracy, flexibility, data requirements, managerial usefulness, and strategic value. It argues that while traditional variance analysis remains relevant for standardized reporting, control, and accountability, predictive analytics offers superior capability in uncertain, data-rich, and rapidly changing environments. The study further highlights that predictive approaches do not necessarily replace traditional methods but rather complement them by extending the analytical scope of management accounting. Integrating both models can improve planning accuracy, operational responsiveness, and strategic decision-making. The review concludes that the future of variance analysis lies in hybrid frameworks that combine the interpretability and control orientation of traditional techniques with the predictive power and adaptability of advanced analytics. Such integration is essential for organizations seeking to strengthen performance management, improve forecasting quality, and achieve competitive advantage in increasingly complex markets. Furthermore, the review emphasizes the need for management accountants to develop analytical, technological, and interpretive skills required to apply predictive tools effectively. Advancing this capability will support the transformation of management accounting from a primarily diagnostic function into a more strategic, anticipatory, and value-creating discipline within modern organizations globally today.