The firmware updates are among the riskiest models of lifecycle management of residential gateway devices and distributed mesh routers, in which a single failure to do so correctly can cause a concurrent disruption to broadband connectivity to large groups of subscribers. The self-healing, adaptive framework of the firmware upgrade system introduced here implements AI-based post-activation validation that is directly integrated into the transactional lifecycle of the TR-369 User Services Platform standard and creates a feedback loop between pre-upgrade telemetry baselines and real-time post-activation performance measurements that are part of the TR-181 data model. The framework, based wholly on open platform architectures, such as prplOS, prplMesh, and EasyMesh, is able to identify performance regressions across multi-access-point network topologies and rollback operations at the node level before degraded firmware is committed permanently. Learning deployment-specific behavioral thresholds and tightening decision boundaries based on the outcomes of historical upgrades gradually causes the system to minimize unnecessary rollbacks and missed detections of failures, enabling quantifiable increases in the reliability of upgrades, service continuity, and stability in the entire network without relying on proprietary management infrastructure.