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This research presents a meta-learning framework that is meant to raise the adaptability and cognitive level of TinyML models that are spread over heterogeneous IoT sensor environments to a considerable extent. The performance of conventional TinyML models after deployment is often lower because these models are confronted with new sensor types, data changes, or environmental variability. To get over these problems, the suggested system uses model-agnostic metalearning (MAML) so that the model can “learn how to learn”. The model is not trained only on the fixed tasks, but it is rather prepared to very quickly adjust to a new task with just a few samples. In this way, it is perfectly capable of working in the real-world IoT scenario where data is often limited, noisy, and heterogeneous. This rapid adaptation ability gives the system the freedom to transfer knowledge in an efficient manner across different sensors; thus, the system's reliability and robustness are greatly improved. In order to allow the system to be actually put into use on the edge devices, which have limited resources, the framework employs quantized neural network architectures that can save memory, reduce power consumption, and lower the computational load without losing accuracy. With this optimization, the system can perform real-time inference and also be updated promptly to accommodate the new data and the changed conditions. This kind of continuous learning is a guarantee that IoT applications environmental sensing, for example, or industrial monitoring will be able to rely on the consistency of the performance and its correctness even when the operational contexts are changing.