Systems and methods are provided for AI-driven predictive firmware update orchestration with self-healing capabilities for a fleet of Internet of Things (IoT) devices, embedded devices, resource-constrained devices, and the like. A device trust manager, according to one implementation, includes a risk assessment module employing machine learning models trained on historical update outcomes, configured to a) receive telemetry data and operational parameters from an IoT device fleet and b) generate a success probability score associated with deploying a firmware update to one or more IoT devices of the IoT device fleet. The device trust manager also includes a scheduling engine configured to determine an optimized update schedule based on the telemetry data, operational parameters, and success probability score, and an update deployment module configured to deploy the firmware update to the one or more IoT devices according to the optimized update schedule.