A smart maintenance system for household appliances is developed by integrating multimodal sensor data with adaptive user profiling to enhance diagnostic accuracy and repair outcomes. The system processes electrical, mechanical, thermal, and fluidic telemetry streams through a probabilistic diagnostic engine, combining fault codes, historical records, and environmental parameters to identify failure types. A human‑adaptive module quantifies user proficiency using success rates, certifications, and behavioral indicators, generating a capability score that guides either self‑repair instructions or technician dispatch. Validation with 1000 participants across five appliance categories showed a 93.3% diagnostic accuracy and an 85% precision in user assessment. Operational efficiency improved significantly, with repair duration reduced by 62.5% and unnecessary technician visits decreased by 61.1%. These results show that IoT‑enabled diagnostics, when coupled with explainable user profiling, can be used to develop a scalable hybrid service model to extend appliance lifespans, reduce manufacturer costs, and empower consumers through safe, transparent, and personalized maintenance pathways. Sensor networks can be used as diagnostic tools as well as the enablers of human‑centered decision‑making, advancing sensor technology toward resilient, bidirectional cyber–physical ecosystems.