Published Paper Details:

Legacy system migration and modernization are critical processes for organizations seeking to stay competitive in an increasingly digital world. This review introduces a novel model for legacy system migration that integrates predictive analytics, machine learning, and a multidimensional data approach, combining technical, operational, and organizational data. By comparing this model with traditional Waterfall and Agile methodologies, we demonstrate its enhanced predictive capabilities, adaptability to emerging technologies, and cost-effectiveness. The proposed framework offers significant improvements in risk management, scalability, and stakeholder engagement, addressing gaps in existing models. Furthermore, this paper explores the implications of these advancements for both practitioners and policymakers, providing actionable recommendations for successful legacy system modernization. Future research directions include the integration of additional emerging technologies, longitudinal migration outcomes, and a deeper understanding of human and organizational factors. This review aims to inform researchers, decision-makers, and industry professionals about the latest advancements and provide guidance for more reliable and efficient system migration strategies