## 1 Introduction  
Predictive maintenance has emerged as an increasingly important strategy for optimizing the operation of complex industrial systems, enabling the anticipation of failures and the prevention of unforeseen downtime. However, current solutions face significant challenges. The integration of data from diverse sources, the complexity of predictive models, and the lack of scalability in conventional approaches continue to pose substantial obstacles. For instance, in the automotive industry, failures in high-precision machinery often lead to extended downtimes, increasing operational costs and affecting production. In the cardboard packaging industry, failures in critical equipment can halt entire production lines, resulting in unforeseen costs, delivery delays, and a significant loss of competitiveness. These issues underscore the urgent need for more effective and adaptable solutions in predictive maintenance [1]. Although current technologies enable more accurate failure predictions, the implementation of solutions remains a challenge, particularly due to data integration, the complexity of predictive models, and the lack of scalable approaches. The digitization of industrial operations and the growing adoption of automation have driven innovation in this field. Robotic process automation (RPA) has emerged as an efficient solution to automate repetitive, rule-based tasks, freeing up resources for more strategic activities and enabling greater precision in operations. When combined with machine learning (ML) techniques, RPA not only improves failure prediction accuracy but also enhances operational agility and efficiency [2]. In this context, this article proposes an innovative and sustainable model that integrates RPA and ML for failure prediction and the improvement of operational efficiency in predictive maintenance. The model aims not only to increase the accuracy of predictions but also to promote sustainability in its three dimensions, which are environmental, social, and economic. Environmentally, the integration of these technologies can significantly reduce resource consumption and CO2 emissions by preventing unexpected failures and optimizing energy and material use. Socially, the model proposes a better allocation of human resources, enabling workers to focus on more strategic tasks, such as data analysis and innovation, rather than dealing with repetitive tasks or emergency issues. Economically, the reduction in operational and maintenance costs can enhance competitiveness and profitability, contributing to long-term financial sustainability [2]. The specific problem addressed by this study is the inefficiency of traditional predictive maintenance systems. These models often fail to anticipate failures effectively, leading to unforeseen downtimes and high maintenance costs. The proposed RPA and ML model seeks to overcome these limitations, allowing for accurate failure prediction and optimized resource management. For example, in a company within the cardboard packaging sector, the implementation of this model resulted in a 66.67% reduction in unplanned downtime events and a 52% decrease in average repair costs, leading to greater efficiency and profitability. The need to adopt the RPA and ML model is evident, given the increasing reliance on technological solutions in industries. Traditional predictive maintenance models are often neither scalable nor sufficiently accurate for the demands of modern industrial operations. The integration of automation and real-time advanced data analytics enables swift and effective intervention while also offering flexibility to adapt to different contexts and industrial sectors [3]. The unique contribution of this work lies in the practical and innovative application of the integrated RPA and ML model in predictive maintenance.