The scope of this work is to present a novel and comprehensive strategy for structural health monitoring (SHM), the focal aspect of which is the technological innovation made in the design and performance of the MonStr data acquisition sensors. The SHM method we propose benefits from the use of a variety of products designed by ASDEA Hardware and ASDEA Software for each specific task in the complex chain of operations needed to obtain near-real-time, reliable outputs for the assessment of structural health conditions. This paper illustrates the main advantages derived from the installation of a MonStr sensor network in terms of signal sampling, noise reduction, and synchronization management. Emphasis is also placed on how the proposed system extracts information from the tremendous amount of data collected by these high-performing devices, as this requires carefully configured algorithms and fast units for computing. The strategy used for the algorithms is briefly presented, and it combines all the usually occurring passages necessary for SHM with deep learning tools provided by Python for parallel GPU computing for the purposes of feature classification and anomaly detection. Global performance of the system is rendered even more efficient through the adoption of a common data format and shared environment provided by the STKO software. Finally, the OpenSees FEM solvers and the STKO pre and postprocessors allow for the construction of a digital twin of the structure under examination, which can then be exposed to what-if analyses and used to gauge the reliability of system alerts.