This adaptation delves into an approach based on technologies such as AI and IoT, which allows companies to respond in an agile and proactive manner to both maintenance needs and customer demands. This contributes to developing a more efficient warranty management model aligned with international best practices and adapted to the challenges of the current industrial sector. The topic of this paper is aligned with the knowledge and skills developed in the field of industrial organization and business management. A fundamental theoretical and practical basis is provided that addresses failure modelling, maintenance policy optimization, and the design of risk and replacement models. These concepts are essential for advanced warranty management, facilitating the prediction and resolution of failures more effectively and reducing the costs associated with unexpected maintenance. In addition, this approach is complemented by providing critical tools for the interpretation of real-time data, which is necessary in the management of performance and maintenance indicators. By employing emerging technologies like machine learning and digital twins (DT), organizations can employ predictive models in the aftersales service value chain, improving the accuracy of interventions and optimizing the life cycle of assets. In this way, the review applied in this study not only modernizes the warranty management framework, but also responds to the strategic needs of the industrial sector, promoting the competitiveness and sustainability of organizations in an environment of constant change. The objective of this study is to provide a structured review of existing methodologies, synthesizing current research and best practices in digital transformation for warranty management. Rather than introducing entirely new approaches, this work integrates and systematizes knowledge on how artificial intelligence (AI), the Internet of Things (IoT), and digital twins enhance predictive and proactive warranty strategies. By analyzing key frameworks and industry applications, this study aims to offer a comprehensive perspective on optimizing warranty management through digital enablers. To achieve this purpose, the following specific objectives are set:  
* Identify and evaluate key technologies (AI, IoT, digital twins, and Computer Maintenance Management Systems) in warranty management, emphasizing their role in transitioning from reactive to predictive maintenance. This objective is achieved through a structured synthesis of the specialized literature and industry applications. * Assess the impact of advanced digital technologies on reducing operational costs, improving response times, and enhancing customer satisfaction in warranty services. This involves analyzing case studies and empirical research to illustrate how digital tools optimize warranty-related decision-making. * Examine how digital enablers align with industry standards and best practices, including ISO 55000 (Asset Management [3]), ISO 9001 (Quality Management [4]), and ISO 42001 (Occupational health and safety Management [5]). This assessment ensures that digital warranty models adhere to regulatory compliance, risk management, and ethical AI implementation across the automotive, railway, and aeronautics sectors. This study contributes to the field by providing a structured, integrative review that consolidates state-of-the-art research and industry applications, supporting the practical implementation of digital transformation in warranty management.