Passive Optical Networks (PONs) constitute the dominant architecture in the last mile that effectively realize the Fiber To The Home/Building/Curve (FTTH/B/C) paradigm. It combines a cost- effective infrastructure with an effective data delivering, where multi- ple users are able to use high-quality services. The latest new generation PON (NG-PON) standard, known as 10-gigabit-capable passive opti- cal network (XG-PON), stands a very promising framework that incor-porates 10 Gbps nominal speed in the downstream direction. In the opposite, all users have to share the upstream channel, where multiple upstream traffic flows are delivered to the Central Office (CO), using a channel of 2.5 Gbps rate. Having in mind that in dense, urban areas the number of users is quite large, an efficient Dynamic Bandwidth Alloca-tion (DBA) scheme is mandatory to guarantee unhindered high-quality service delivery. In this work, a resilient coordination scheme is presented that intends to ensure high-efficient traffic delivery under pressing traffic conditions. In order to achieve that, a sophisticated machine learning model is proposed that coordinates the Optical Networks Units (ONUs) based on their traffic profile. The proposed, Adaptive Resilient Estima-tion Scheme (ARES), contributes in a twofold way. First, it succeeds to provide balanced resource allocation, under heavy traffic circumstances, by isolating idle ONUs. Second, it manages to effectively adjust the amount of fixed bandwidth allocated to Alloc-IDs based on their traf-fic behavior. Simulation results demonstrate that ARES offers consider-able improvements in terms of average upstream packet delay and traffic received, while the estimation accuracy attains at high levels.