Modern CMOS technologies such as FDSOI are affected by severe aging effects that do not only depend on physical issues related to nanoscale technologies, but also on the circuit environment and its run-time activity. Therefore it is extremely difficult to reliably establish a-priori guard bands for Critical Path estimations, usually leading to both large delay penalties (and therefore loss of performances) or too short operating lifetime. In this paper, we propose an approach that uses Machine Learning techniques to obtain reliable predictions of the aging of the Near Critical Paths. Starting from a limited set of measurements and simulation data, our framework is able to accurately estimate Critical Path delay degradation in time depending on physical parameters, environment conditions and circuit activity. Further to that, the corresponding regression models are applied to obtain dynamic aging-aware Operating Performance Point selection strategies.