The manufacturing process can be classified as one of the most crucial for the functioning of the industry, as it forms the foundation of the entire industrial mechanism. Over the years, with the advent of Industry 4.0, this process has been refined, achieving a high degree of precision. The pillar of Industry 4.0 is the sensors that perform data acquisition and enable subsequent analyses. One of the sensors used in this process is the Acoustic Emission (AE) sensor; however, its current use in the industry is highly complex, and still in the embryonic stage, relying on multiple software and computational tools for data acquisition and processing. In this context, this work proposes a system comprising hardware and embedded software designed to facilitate the acquisition of acoustic emission signals through a developing wireless IoT sensor. This system is part of a robust ecosystem, designed to support the implementation of fault diagnosis models, feature extraction, pattern classification, and integration with cloud storage systems. The results demonstrate that the developed system has become a viable solution within an ecosystem for applications using wireless IoT acoustic emission sensors, reducing all complex apparatus to a single tool. Moreover, it enabled remote configuration of the acoustic emission sensor during tests, supporting the inclusion of mathematical and computational models for feature extraction and failure analysis, allowing the registration and organization of tests through forms for registration and document management, without demanding external computers. Such contributions have allowed for the expansion of the use of acoustic emission sensors (AE), aligned with the demands of Industry 4.0, and have promoted significant advancements in the application of IoT sensors while contributing to the efficiency of manufacturing processes.